Workshop Details

BioDM: Workshop on Biological Data Mining and its Applications in Healthcare

Organized by Xiao-Li Li, See-Kiong Ng and Jason T. L. Wang
08:30 - 17:40
Room: Permeke
http://www1.i2r.a-star.edu.sg/~xlli/BioDM.html

In this BioDM workshop, we address the challenging issues in various biological and healthcare data analysis. The workshop, held in Brussels Belgium on December 10 2012, has garnered great response from the researchers and received a total of 29 paper submissions (16 workshop submissions; 13 transferred main conference submissions), out of which 14 (48.3%) were selected for presentation at the workshop. We would like to thank all the authors who have submitted their papers on many exciting and important research topics. We also thank the presenters of the accepted papers. Last but not least, we thank all the workshop participants for attending this third workshop in Brussels Belgium. It is our hope that the workshop will provide a lasting platform for disseminating the latest research results and practice of the data mining approaches in biology and healthcare.

event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?> event_type == 1) {?> event_type == 2) {?> event_type == 3) {?> event_type == 4) {?> event_type == 5) {?>

08:30 - 08:50	Welcoming and Introduction Xiao-Li Li	08:30 - 08:50	Welcoming and Introduction Xiao-Li Li	08:30 - 08:50	Welcoming and Introduction	Welcoming and Introduction	08:30 - 08:50	Welcoming and Introduction Xiao-Li Li
08:50 - 09:40	Invited Talk: Modeling complex diseases using discriminative network fragments Ambuj Singh, University of California at Santa Barbara	08:50 - 09:40	Invited Talk: Modeling complex diseases using discriminative network fragments Ambuj Singh, University of California at Santa Barbara	08:50 - 09:40	Invited Talk: Modeling complex diseases using discriminative network fragments	Invited Talk: Modeling complex diseases using discriminative network fragments	08:50 - 09:40	Invited Talk: Modeling complex diseases using discriminative network fragments Ambuj Singh, University of California at Santa Barbara
09:40 - 12:30	Morning Session: Classification, Decision making, Visualization	09:40 - 12:30	Morning Session: Classification, Decision making, Visualization	09:40 - 12:30	Morning Session: Classification, Decision making, Visualization	Morning Session: Classification, Decision making, Visualization	09:40 - 12:30	Morning Session: Classification, Decision making, Visualization
09:40 - 10:00	Adapting Surgical Models to Individual Hospitals using Transfer Learning Gyemin Lee, Ilan Rubinfeld, and Zeeshan Syed	09:40 - 10:00	Adapting Surgical Models to Individual Hospitals using Transfer Learning Gyemin Lee, Ilan Rubinfeld, and Zeeshan Syed	09:40 - 10:00	Adapting Surgical Models to Individual Hospitals using Transfer Learning	Adapting Surgical Models to Individual Hospitals using Transfer Learning	09:40 - 10:00	Adapting Surgical Models to Individual Hospitals using Transfer Learning Gyemin Lee, Ilan Rubinfeld, and Zeeshan Syed
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 10:50	Mining medical data to develop clinical decision making tools in hemodialysis - Prediction of cardiovascular events in incident hemodialysis patients: Jasmine Ion Titapiccolo, Manuela Ferrario, Maria Gabriella Signorini, Sergio Cerutti, Carlo Barbieri, Flavio Mari, and Emanuele Gatti	10:30 - 10:50	Mining medical data to develop clinical decision making tools in hemodialysis - Prediction of cardiovascular events in incident hemodialysis patients: Jasmine Ion Titapiccolo, Manuela Ferrario, Maria Gabriella Signorini, Sergio Cerutti, Carlo Barbieri, Flavio Mari, and Emanuele Gatti	10:30 - 10:50	Mining medical data to develop clinical decision making tools in hemodialysis - Prediction of cardiovascular events in incident hemodialysis patients:	Mining medical data to develop clinical decision making tools in hemodialysis - Prediction of cardiovascular events in incident hemodialysis patients:	10:30 - 10:50	Mining medical data to develop clinical decision making tools in hemodialysis - Prediction of cardiovascular events in incident hemodialysis patients: Jasmine Ion Titapiccolo, Manuela Ferrario, Maria Gabriella Signorini, Sergio Cerutti, Carlo Barbieri, Flavio Mari, and Emanuele Gatti
10:50 - 11:10	Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair Brigida Monica Faria	10:50 - 11:10	Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair Brigida Monica Faria	10:50 - 11:10	Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair	Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair	10:50 - 11:10	Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair Brigida Monica Faria
11:10 - 11:30	Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms Roohallah Alizadehsani, Mohammad Javad Hosseini, Zahra Alizadeh Sani, Asma Ghandeharioun, and Reihane Boghrati	11:10 - 11:30	Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms Roohallah Alizadehsani, Mohammad Javad Hosseini, Zahra Alizadeh Sani, Asma Ghandeharioun, and Reihane Boghrati	11:10 - 11:30	Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms	Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms	11:10 - 11:30	Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms Roohallah Alizadehsani, Mohammad Javad Hosseini, Zahra Alizadeh Sani, Asma Ghandeharioun, and Reihane Boghrati
11:30 - 11:50	Evidence Theory-based Approach for Epileptic Seizure Detection Abduljalil Mohamed, Khaled Shaban, and Amr Mohamed	11:30 - 11:50	Evidence Theory-based Approach for Epileptic Seizure Detection Abduljalil Mohamed, Khaled Shaban, and Amr Mohamed	11:30 - 11:50	Evidence Theory-based Approach for Epileptic Seizure Detection	Evidence Theory-based Approach for Epileptic Seizure Detection	11:30 - 11:50	Evidence Theory-based Approach for Epileptic Seizure Detection Abduljalil Mohamed, Khaled Shaban, and Amr Mohamed
11:50 - 12:10	Predicting Hospital Length of Stay (PHLOS) : A Multi-Tiered Data Mining approach Ali Azari, Vandana P. Janeja, and Alex Mohseni	11:50 - 12:10	Predicting Hospital Length of Stay (PHLOS) : A Multi-Tiered Data Mining approach Ali Azari, Vandana P. Janeja, and Alex Mohseni	11:50 - 12:10	Predicting Hospital Length of Stay (PHLOS) : A Multi-Tiered Data Mining approach	Predicting Hospital Length of Stay (PHLOS) : A Multi-Tiered Data Mining approach	11:50 - 12:10	Predicting Hospital Length of Stay (PHLOS) : A Multi-Tiered Data Mining approach Ali Azari, Vandana P. Janeja, and Alex Mohseni
12:10 - 12:30	Using perspective wall to visualize medical data in the Intensive Care Unit Hela LTIFI, Mounir Ben Ayed, Ghada Trabelsi, and M. Adel ALIMI	12:10 - 12:30	Using perspective wall to visualize medical data in the Intensive Care Unit Hela LTIFI, Mounir Ben Ayed, Ghada Trabelsi, and M. Adel ALIMI	12:10 - 12:30	Using perspective wall to visualize medical data in the Intensive Care Unit	Using perspective wall to visualize medical data in the Intensive Care Unit	12:10 - 12:30	Using perspective wall to visualize medical data in the Intensive Care Unit Hela LTIFI, Mounir Ben Ayed, Ghada Trabelsi, and M. Adel ALIMI
12:30 - 14:00	Lunch Break	12:30 - 14:00	Lunch Break	12:30 - 14:00	Lunch Break	Lunch Break	12:30 - 14:00	Lunch Break
14:00 - 14:50	Invited Talk: Perspectives of feature selection in bioinformatics: from relevance to causal inference Bontempi Gianluca, Université Libre de Bruxelles	14:00 - 14:50	Invited Talk: Perspectives of feature selection in bioinformatics: from relevance to causal inference Bontempi Gianluca, Université Libre de Bruxelles	14:00 - 14:50	Invited Talk: Perspectives of feature selection in bioinformatics: from relevance to causal inference	Invited Talk: Perspectives of feature selection in bioinformatics: from relevance to causal inference	14:00 - 14:50	Invited Talk: Perspectives of feature selection in bioinformatics: from relevance to causal inference Bontempi Gianluca, Université Libre de Bruxelles
14:50 - 17:40	Afternoon Session: Feature selection, Clustering, Data fusion, Retrieval, Graph mining	14:50 - 17:40	Afternoon Session: Feature selection, Clustering, Data fusion, Retrieval, Graph mining	14:50 - 17:40	Afternoon Session: Feature selection, Clustering, Data fusion, Retrieval, Graph mining	Afternoon Session: Feature selection, Clustering, Data fusion, Retrieval, Graph mining	14:50 - 17:40	Afternoon Session: Feature selection, Clustering, Data fusion, Retrieval, Graph mining
14:50 - 15:10	Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics Evrim Acar, Gozde Gurdeniz, Morten A. Rasmussen, Daniela Rago, Lars O. Dragsted, and Rasmus Bro	14:50 - 15:10	Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics Evrim Acar, Gozde Gurdeniz, Morten A. Rasmussen, Daniela Rago, Lars O. Dragsted, and Rasmus Bro	14:50 - 15:10	Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics	Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics	14:50 - 15:10	Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics Evrim Acar, Gozde Gurdeniz, Morten A. Rasmussen, Daniela Rago, Lars O. Dragsted, and Rasmus Bro
15:10 - 15:30	Clustering Tandem Repeats Via Trinucleotides Yupu Liang, Dina Sokol, and Sarah Zelikovitz	15:10 - 15:30	Clustering Tandem Repeats Via Trinucleotides Yupu Liang, Dina Sokol, and Sarah Zelikovitz	15:10 - 15:30	Clustering Tandem Repeats Via Trinucleotides	Clustering Tandem Repeats Via Trinucleotides	15:10 - 15:30	Clustering Tandem Repeats Via Trinucleotides Yupu Liang, Dina Sokol, and Sarah Zelikovitz
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 16:20	Evaluation of Feature Ranking Ensembles for High-Dimensional Biomedical Data: A Case Study Ludmila Kuncheva, Christopher Smith, Yasir Syed, Christopher Phillips, and Keir Lewis	16:00 - 16:20	Evaluation of Feature Ranking Ensembles for High-Dimensional Biomedical Data: A Case Study Ludmila Kuncheva, Christopher Smith, Yasir Syed, Christopher Phillips, and Keir Lewis	16:00 - 16:20	Evaluation of Feature Ranking Ensembles for High-Dimensional Biomedical Data: A Case Study	Evaluation of Feature Ranking Ensembles for High-Dimensional Biomedical Data: A Case Study	16:00 - 16:20	Evaluation of Feature Ranking Ensembles for High-Dimensional Biomedical Data: A Case Study Ludmila Kuncheva, Christopher Smith, Yasir Syed, Christopher Phillips, and Keir Lewis
16:20 - 16:40	Improved Feature Selection by Incorporating Gene Similarity into the LASSO Christopher Gillies, Xiaoli Gao, Nilesh Patel, Mohammad Siadat, and George Wilson	16:20 - 16:40	Improved Feature Selection by Incorporating Gene Similarity into the LASSO Christopher Gillies, Xiaoli Gao, Nilesh Patel, Mohammad Siadat, and George Wilson	16:20 - 16:40	Improved Feature Selection by Incorporating Gene Similarity into the LASSO	Improved Feature Selection by Incorporating Gene Similarity into the LASSO	16:20 - 16:40	Improved Feature Selection by Incorporating Gene Similarity into the LASSO Christopher Gillies, Xiaoli Gao, Nilesh Patel, Mohammad Siadat, and George Wilson
16:40 - 17:00	Discovering Aberrant Patterns of Human Connectcome in Alzheimerís Disease via Subgraph Mining Junming Shao	16:40 - 17:00	Discovering Aberrant Patterns of Human Connectcome in Alzheimerís Disease via Subgraph Mining Junming Shao	16:40 - 17:00	Discovering Aberrant Patterns of Human Connectcome in Alzheimerís Disease via Subgraph Mining	Discovering Aberrant Patterns of Human Connectcome in Alzheimerís Disease via Subgraph Mining	16:40 - 17:00	Discovering Aberrant Patterns of Human Connectcome in Alzheimerís Disease via Subgraph Mining Junming Shao
17:00 - 17:20	Figure Retrieval in Biomedical Literature P Radha Krishna, K Sai Deepak, and Harikrishna G N Rai	17:00 - 17:20	Figure Retrieval in Biomedical Literature P Radha Krishna, K Sai Deepak, and Harikrishna G N Rai	17:00 - 17:20	Figure Retrieval in Biomedical Literature	Figure Retrieval in Biomedical Literature	17:00 - 17:20	Figure Retrieval in Biomedical Literature P Radha Krishna, K Sai Deepak, and Harikrishna G N Rai
17:20 - 17:40	Discovering aging-genes by topological features in Drosophila melanogaster protein--protein interaction network Xin Song, Yuan-Chun Zhou, Kai Feng, Yan-Hui Li, and Jian-Hui Li	17:20 - 17:40	Discovering aging-genes by topological features in Drosophila melanogaster protein--protein interaction network Xin Song, Yuan-Chun Zhou, Kai Feng, Yan-Hui Li, and Jian-Hui Li	17:20 - 17:40	Discovering aging-genes by topological features in Drosophila melanogaster protein--protein interaction network	Discovering aging-genes by topological features in Drosophila melanogaster protein--protein interaction network	17:20 - 17:40	Discovering aging-genes by topological features in Drosophila melanogaster protein--protein interaction network Xin Song, Yuan-Chun Zhou, Kai Feng, Yan-Hui Li, and Jian-Hui Li

COSTS: Workshop on Cost Sensitive Data Mining

Organized by Sunil Vadera, Mohamad Saraee, Susan Lomax
14:00 - 18:00
Room: Turner
https://sites.google.com/site/ieeecostsensitive

Most real world applications need to take account of costs, whether it is the cost of obtaining data, costs associated with applying a data mining algorithm or the financial and legal implications of misclassification. Thus, for example, in marketing, we are interested in identifying investments that produce greatest income given the cost of marketing. In medical diagnosis we need to take account of the costs of tests required in diagnosis, such as blood tests, x-rays and scans.

This workshop includes papers that explore topics such as learning rules that take account of costs of misclassification, cost sensitive unsupervised learning, the use of utility and game theory in balancing costs and benefits, active learning for minimising costs of acquiring information and even how to take account of costs when they are missing. The workshop will provide a useful opportunity to understand some of the key issues in cost sensitive data mining and current direction of research in this field.

14:00 - 14:10	Welcome and Introduction Sunil Vadera	14:00 - 14:10	Welcome and Introduction Sunil Vadera	14:00 - 14:10	Welcome and Introduction	Welcome and Introduction	14:00 - 14:10	Welcome and Introduction Sunil Vadera
14:10 - 14:40	Invited Talk: Cost Sensitive Action Rule Mining Hendrik Blockeel	14:10 - 14:40	Invited Talk: Cost Sensitive Action Rule Mining Hendrik Blockeel	14:10 - 14:40	Invited Talk: Cost Sensitive Action Rule Mining	Invited Talk: Cost Sensitive Action Rule Mining	14:10 - 14:40	Invited Talk: Cost Sensitive Action Rule Mining Hendrik Blockeel
14:40 - 15:05	A Weighted SOM for classifying data with instance-varying importance Peter Sarlin	14:40 - 15:05	A Weighted SOM for classifying data with instance-varying importance Peter Sarlin	14:40 - 15:05	A Weighted SOM for classifying data with instance-varying importance	A Weighted SOM for classifying data with instance-varying importance	14:40 - 15:05	A Weighted SOM for classifying data with instance-varying importance Peter Sarlin
15:05 - 15:30	When Additional Views Are Not Free: Active View Completion for Multi-View Semi-Supervised Learning Brian Quanz and Jun Huan	15:05 - 15:30	When Additional Views Are Not Free: Active View Completion for Multi-View Semi-Supervised Learning Brian Quanz and Jun Huan	15:05 - 15:30	When Additional Views Are Not Free: Active View Completion for Multi-View Semi-Supervised Learning	When Additional Views Are Not Free: Active View Completion for Multi-View Semi-Supervised Learning	15:05 - 15:30	When Additional Views Are Not Free: Active View Completion for Multi-View Semi-Supervised Learning Brian Quanz and Jun Huan
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 16:25	A Multi-Armed Bandit Approach to Cost-Sensitive Decision Tree Learning Susan Lomax, Sunil Vadera, Mohamad Saraee	16:00 - 16:25	A Multi-Armed Bandit Approach to Cost-Sensitive Decision Tree Learning Susan Lomax, Sunil Vadera, Mohamad Saraee	16:00 - 16:25	A Multi-Armed Bandit Approach to Cost-Sensitive Decision Tree Learning	A Multi-Armed Bandit Approach to Cost-Sensitive Decision Tree Learning	16:00 - 16:25	A Multi-Armed Bandit Approach to Cost-Sensitive Decision Tree Learning Susan Lomax, Sunil Vadera, Mohamad Saraee
16:25 - 16:50	Learning in the Class Imbalance Problem When Costs are Unknown for Errors and Rejects Xiaowan Zhang and Baogang Hu	16:25 - 16:50	Learning in the Class Imbalance Problem When Costs are Unknown for Errors and Rejects Xiaowan Zhang and Baogang Hu	16:25 - 16:50	Learning in the Class Imbalance Problem When Costs are Unknown for Errors and Rejects	Learning in the Class Imbalance Problem When Costs are Unknown for Errors and Rejects	16:25 - 16:50	Learning in the Class Imbalance Problem When Costs are Unknown for Errors and Rejects Xiaowan Zhang and Baogang Hu
16:50 - 17:15	Learning Cost-Sensitive Rules for Non-Forced Classification Arjun Bakshi and Raj Bhatnagar	16:50 - 17:15	Learning Cost-Sensitive Rules for Non-Forced Classification Arjun Bakshi and Raj Bhatnagar	16:50 - 17:15	Learning Cost-Sensitive Rules for Non-Forced Classification	Learning Cost-Sensitive Rules for Non-Forced Classification	16:50 - 17:15	Learning Cost-Sensitive Rules for Non-Forced Classification Arjun Bakshi and Raj Bhatnagar
17:15 - 17:40	Towards Utility Maximization in Regression Rita P. Ribeiro	17:15 - 17:40	Towards Utility Maximization in Regression Rita P. Ribeiro	17:15 - 17:40	Towards Utility Maximization in Regression	Towards Utility Maximization in Regression	17:15 - 17:40	Towards Utility Maximization in Regression Rita P. Ribeiro
17:40 - 18:00	Discussion and Closing Remarks	17:40 - 18:00	Discussion and Closing Remarks	17:40 - 18:00	Discussion and Closing Remarks	Discussion and Closing Remarks	17:40 - 18:00	Discussion and Closing Remarks

CPROD1: Workshop on the ICDM 2012 Contest on Consumer PRODuct Mention Recognition and Linking

Organized by Gabor Melli, Christian Romming, and Yabo (Arber) Xu
14:00 - 18:00
Room: Alto & Mezzo
http://www.gabormelli.com/Projects/CPROD1workshop

A significant portion of user generated content on the web discusses consumer products. The CPROD1 workshop presents the state-of-the-art algorithms that ranked highest in the ICDM-2012 contest that required contestants to automatically recognize mentions of consumer products in previously unseen user generated web content, and to link each mention to the corresponding set of products in a large product catalog. Join us to discuss the future of text analytics.

14:00 - 14:05	Welcoming and Introduction Gabor Melli	14:00 - 14:05	Welcoming and Introduction Gabor Melli	14:00 - 14:05	Welcoming and Introduction	Welcoming and Introduction	14:00 - 14:05	Welcoming and Introduction Gabor Melli
14:05 - 14:30	An Overview of the CPROD1 Contest on Consumer Product Recognition within User Generated Postings and Normalization against a Large Product Catalog Gabor Melli, and Christian Romming	14:05 - 14:30	An Overview of the CPROD1 Contest on Consumer Product Recognition within User Generated Postings and Normalization against a Large Product Catalog Gabor Melli, and Christian Romming	14:05 - 14:30	An Overview of the CPROD1 Contest on Consumer Product Recognition within User Generated Postings and Normalization against a Large Product Catalog	An Overview of the CPROD1 Contest on Consumer Product Recognition within User Generated Postings and Normalization against a Large Product Catalog	14:05 - 14:30	An Overview of the CPROD1 Contest on Consumer Product Recognition within User Generated Postings and Normalization against a Large Product Catalog Gabor Melli, and Christian Romming
14:30 - 15:00	Accurate Product Name Recognition from User Generated Content Sen Wu, Zhanpeng Fang, and Jie Tang	14:30 - 15:00	Accurate Product Name Recognition from User Generated Content Sen Wu, Zhanpeng Fang, and Jie Tang 1st place winner presentation	14:30 - 15:00	Accurate Product Name Recognition from User Generated Content	Accurate Product Name Recognition from User Generated Content	14:30 - 15:00	Accurate Product Name Recognition from User Generated Content Sen Wu, Zhanpeng Fang, and Jie Tang
15:00 - 15:30	Identification and Disambiguation of Product Mentions with Information Retrieval and Problem Specific Methods Olexandr Topchylo	15:00 - 15:30	Identification and Disambiguation of Product Mentions with Information Retrieval and Problem Specific Methods Olexandr Topchylo 2nd place winner presentation by Dafne van Kuppevelt	15:00 - 15:30	Identification and Disambiguation of Product Mentions with Information Retrieval and Problem Specific Methods	Identification and Disambiguation of Product Mentions with Information Retrieval and Problem Specific Methods	15:00 - 15:30	Identification and Disambiguation of Product Mentions with Information Retrieval and Problem Specific Methods Olexandr Topchylo
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 16:30	An NER-based Product Identification and Lucene-based Product Linking Approach to CPROD1 Challenge Zhiqiang Toh, Wenting Wang, Man Lan, and Xiaoli Li	16:00 - 16:30	An NER-based Product Identification and Lucene-based Product Linking Approach to CPROD1 Challenge Zhiqiang Toh, Wenting Wang, Man Lan, and Xiaoli Li 5th place presentation	16:00 - 16:30	An NER-based Product Identification and Lucene-based Product Linking Approach to CPROD1 Challenge	An NER-based Product Identification and Lucene-based Product Linking Approach to CPROD1 Challenge	16:00 - 16:30	An NER-based Product Identification and Lucene-based Product Linking Approach to CPROD1 Challenge Zhiqiang Toh, Wenting Wang, Man Lan, and Xiaoli Li
16:30 - 17:00	Invited Talk: From Case Files to Document Structure Wauter Bosma	16:30 - 17:00	Invited Talk: From Case Files to Document Structure Wauter Bosma	16:30 - 17:00	Invited Talk: From Case Files to Document Structure	Invited Talk: From Case Files to Document Structure	16:30 - 17:00	Invited Talk: From Case Files to Document Structure Wauter Bosma
17:00 - 17:30	An Ensemble-Based Named Entity Recognition Solution for Detecting Consumer Products Łukasz Romaszko	17:00 - 17:30	An Ensemble-Based Named Entity Recognition Solution for Detecting Consumer Products Łukasz Romaszko 3rd place winner presentation	17:00 - 17:30	An Ensemble-Based Named Entity Recognition Solution for Detecting Consumer Products	An Ensemble-Based Named Entity Recognition Solution for Detecting Consumer Products	17:00 - 17:30	An Ensemble-Based Named Entity Recognition Solution for Detecting Consumer Products Łukasz Romaszko
17:30 - 18:00	Rule Based Product Name Recognition and Disambiguation Balázs Gödény	17:30 - 18:00	Rule Based Product Name Recognition and Disambiguation Balázs Gödény 4th place presentation	17:30 - 18:00	Rule Based Product Name Recognition and Disambiguation	Rule Based Product Name Recognition and Disambiguation	17:30 - 18:00	Rule Based Product Name Recognition and Disambiguation Balázs Gödény

DaMNet: Workshop on Data Mining in Networks

Organized by Giuseppe Di Fatta and Antonio Liotta
09:00 - 17:00
Room: Watteau I
http://damnet.reading.ac.uk

The complexity of numerous social, biological, and communication systems is driving many researchers towards the adoption of data mining approaches. The International Workshop on Data Mining in Networks looks specifically at how data mining is currently used for the characterization, management and control of complex networks. This aim is particularly challenging, as it requires a cross-disciplinary approach, pulling together efforts from a variety of research communities with an interest in the analysis and control of complex networks by means of Data Mining approaches.

09:00 - 09:10	Welcoming and Introduction Antonio Liotta	09:00 - 09:10	Welcoming and Introduction Antonio Liotta	09:00 - 09:10	Welcoming and Introduction	Welcoming and Introduction	09:00 - 09:10	Welcoming and Introduction Antonio Liotta
09:10 - 09:35	Motif Mining in Weighted Networks Sarvenz Choobdar, Pedro Ribeiro and Fernando Silva	09:10 - 09:35	Motif Mining in Weighted Networks Sarvenz Choobdar, Pedro Ribeiro and Fernando Silva	09:10 - 09:35	Motif Mining in Weighted Networks	Motif Mining in Weighted Networks	09:10 - 09:35	Motif Mining in Weighted Networks Sarvenz Choobdar, Pedro Ribeiro and Fernando Silva
09:35 - 10:00	Comparison of the Efficiency of MapReduce and Bulk Synchronous Parallel Approaches to Large Network Processing Tomasz Kajdanowicz	09:35 - 10:00	Comparison of the Efficiency of MapReduce and Bulk Synchronous Parallel Approaches to Large Network Processing Tomasz Kajdanowicz	09:35 - 10:00	Comparison of the Efficiency of MapReduce and Bulk Synchronous Parallel Approaches to Large Network Processing	Comparison of the Efficiency of MapReduce and Bulk Synchronous Parallel Approaches to Large Network Processing	09:35 - 10:00	Comparison of the Efficiency of MapReduce and Bulk Synchronous Parallel Approaches to Large Network Processing Tomasz Kajdanowicz
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:35 - 11:00	Canonical Correlation Analysis for Detecting Changes in Network Structure Aidan O'Sullivan, Niall Adams and Iead Rezek	10:35 - 11:00	Canonical Correlation Analysis for Detecting Changes in Network Structure Aidan O'Sullivan, Niall Adams and Iead Rezek	10:35 - 11:00	Canonical Correlation Analysis for Detecting Changes in Network Structure	Canonical Correlation Analysis for Detecting Changes in Network Structure	10:35 - 11:00	Canonical Correlation Analysis for Detecting Changes in Network Structure Aidan O'Sullivan, Niall Adams and Iead Rezek
11:00 - 11:25	Uncovering the Spatio-Temporal Structure of Social Networks using Cell Phone Records Luis G. Moyano, Oscar Ricardo Moll Thomae and Enrique Frias-Martinez	11:00 - 11:25	Uncovering the Spatio-Temporal Structure of Social Networks using Cell Phone Records Luis G. Moyano, Oscar Ricardo Moll Thomae and Enrique Frias-Martinez	11:00 - 11:25	Uncovering the Spatio-Temporal Structure of Social Networks using Cell Phone Records	Uncovering the Spatio-Temporal Structure of Social Networks using Cell Phone Records	11:00 - 11:25	Uncovering the Spatio-Temporal Structure of Social Networks using Cell Phone Records Luis G. Moyano, Oscar Ricardo Moll Thomae and Enrique Frias-Martinez
11:25 - 11:50	Maximizing Information Spread Through Influence Structures in Social Networks Saurav Pandit, Yang Yang and Nitesh Chawla	11:25 - 11:50	Maximizing Information Spread Through Influence Structures in Social Networks Saurav Pandit, Yang Yang and Nitesh Chawla	11:25 - 11:50	Maximizing Information Spread Through Influence Structures in Social Networks	Maximizing Information Spread Through Influence Structures in Social Networks	11:25 - 11:50	Maximizing Information Spread Through Influence Structures in Social Networks Saurav Pandit, Yang Yang and Nitesh Chawla
12:00 - 14:30	Lunch Break	12:00 - 14:30	Lunch Break	12:00 - 14:30	Lunch Break	Lunch Break	12:00 - 14:30	Lunch Break
14:35 - 15:00	Sampling Online Social Networks Using Coupling From The Past Kenton White, Guichong Li and Nathalie Japkowicz	14:35 - 15:00	Sampling Online Social Networks Using Coupling From The Past Kenton White, Guichong Li and Nathalie Japkowicz	14:35 - 15:00	Sampling Online Social Networks Using Coupling From The Past	Sampling Online Social Networks Using Coupling From The Past	14:35 - 15:00	Sampling Online Social Networks Using Coupling From The Past Kenton White, Guichong Li and Nathalie Japkowicz
15:00 - 15:25	EigenSP: A More Accurate Shortest Path Distance Estimation on Large-Scale Networks Koji Maruhashi, Junichi Shigezumi, Nobuhiro Yugami and Christos Faloutsos	15:00 - 15:25	EigenSP: A More Accurate Shortest Path Distance Estimation on Large-Scale Networks Koji Maruhashi, Junichi Shigezumi, Nobuhiro Yugami and Christos Faloutsos	15:00 - 15:25	EigenSP: A More Accurate Shortest Path Distance Estimation on Large-Scale Networks	EigenSP: A More Accurate Shortest Path Distance Estimation on Large-Scale Networks	15:00 - 15:25	EigenSP: A More Accurate Shortest Path Distance Estimation on Large-Scale Networks Koji Maruhashi, Junichi Shigezumi, Nobuhiro Yugami and Christos Faloutsos
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:05 - 16:30	Sensor Network Localization for Moving Sensors Arvind Agarwal, Hal Daume, Jeff M. Phillips and Suresh Venkatasubramanian	16:05 - 16:30	Sensor Network Localization for Moving Sensors Arvind Agarwal, Hal Daume, Jeff M. Phillips and Suresh Venkatasubramanian	16:05 - 16:30	Sensor Network Localization for Moving Sensors	Sensor Network Localization for Moving Sensors	16:05 - 16:30	Sensor Network Localization for Moving Sensors Arvind Agarwal, Hal Daume, Jeff M. Phillips and Suresh Venkatasubramanian
16:30 - 16:55	Effect of Data Repair on Mining Network Streams Ji Meng Loh and Tamraparni Dasu	16:30 - 16:55	Effect of Data Repair on Mining Network Streams Ji Meng Loh and Tamraparni Dasu	16:30 - 16:55	Effect of Data Repair on Mining Network Streams	Effect of Data Repair on Mining Network Streams	16:30 - 16:55	Effect of Data Repair on Mining Network Streams Ji Meng Loh and Tamraparni Dasu
16:55 - 17:00	Conclusive remarks Antonio Liotta	16:55 - 17:00	Conclusive remarks Antonio Liotta	16:55 - 17:00	Conclusive remarks	Conclusive remarks	16:55 - 17:00	Conclusive remarks Antonio Liotta

DMS: Workshop on Data Mining for Service

Organized by Shusaku Tsumoto and Katsutoshi Yada
09:00 - 17:05
Room: Watteau II
http://www2.itc.kansai-u.ac.jp/~yada/conf/dms12

In midst of service applications in engineering and the increasing importance of the service sector in the global economy, services are being scientifically and much attention is being focused on service science as a means to improve productivity and underlying business process. The focus of this workshop is on empirical findings, methodological papers, and theoretical and conceptual insights related to data mining in the field of various service application areas.

09:00 - 09:10	Welcoming and Introduction Shusaku Tsumoto and Katsutoshi Yada	09:00 - 09:10	Welcoming and Introduction Shusaku Tsumoto and Katsutoshi Yada	09:00 - 09:10	Welcoming and Introduction	Welcoming and Introduction	09:00 - 09:10	Welcoming and Introduction Shusaku Tsumoto and Katsutoshi Yada
09:10 - 10:00	Invited Talk: Systems Health Care - The Power of Data Intelligence Dr. Hiroshi Nakajima	09:10 - 10:00	Invited Talk: Systems Health Care - The Power of Data Intelligence Dr. Hiroshi Nakajima	09:10 - 10:00	Invited Talk: Systems Health Care - The Power of Data Intelligence	Invited Talk: Systems Health Care - The Power of Data Intelligence	09:10 - 10:00	Invited Talk: Systems Health Care - The Power of Data Intelligence Dr. Hiroshi Nakajima
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 11:45	Dependencies and Sequences	10:30 - 11:45	Dependencies and Sequences	10:30 - 11:45	Dependencies and Sequences	Dependencies and Sequences	10:30 - 11:45	Dependencies and Sequences
10:30 - 10:55	Exploration of dependencies among sections in a supermarket using a tree-structured graphical model Keiji Takai	10:30 - 10:55	Exploration of dependencies among sections in a supermarket using a tree-structured graphical model Keiji Takai	10:30 - 10:55	Exploration of dependencies among sections in a supermarket using a tree-structured graphical model	Exploration of dependencies among sections in a supermarket using a tree-structured graphical model	10:30 - 10:55	Exploration of dependencies among sections in a supermarket using a tree-structured graphical model Keiji Takai
10:55 - 11:20	Extracting information from sequences of financial ratios with Markov for Discrimination: an application to bankruptcy prediction Andrey Volkov and Dirk Van den Poel	10:55 - 11:20	Extracting information from sequences of financial ratios with Markov for Discrimination: an application to bankruptcy prediction Andrey Volkov and Dirk Van den Poel	10:55 - 11:20	Extracting information from sequences of financial ratios with Markov for Discrimination: an application to bankruptcy prediction	Extracting information from sequences of financial ratios with Markov for Discrimination: an application to bankruptcy prediction	10:55 - 11:20	Extracting information from sequences of financial ratios with Markov for Discrimination: an application to bankruptcy prediction Andrey Volkov and Dirk Van den Poel
11:20 - 11:45	Streamlining Service Levels for IT Infrastructure Support Girish Palshikar, Mohammed Mudassar, Harrick Vin and Maitreya Natu	11:20 - 11:45	Streamlining Service Levels for IT Infrastructure Support Girish Palshikar, Mohammed Mudassar, Harrick Vin and Maitreya Natu	11:20 - 11:45	Streamlining Service Levels for IT Infrastructure Support	Streamlining Service Levels for IT Infrastructure Support	11:20 - 11:45	Streamlining Service Levels for IT Infrastructure Support Girish Palshikar, Mohammed Mudassar, Harrick Vin and Maitreya Natu
11:45 - 14:00	Lunch Break	11:45 - 14:00	Lunch Break	11:45 - 14:00	Lunch Break	Lunch Break	11:45 - 14:00	Lunch Break
14:00 - 15:15	Analytics	14:00 - 15:15	Analytics	14:00 - 15:15	Analytics	Analytics	14:00 - 15:15	Analytics
14:00 - 14:25	Temporary Staffing Services: A Data Mining Perspective Jeroen D'Haen and Dirk Van den Poel	14:00 - 14:25	Temporary Staffing Services: A Data Mining Perspective Jeroen D'Haen and Dirk Van den Poel	14:00 - 14:25	Temporary Staffing Services: A Data Mining Perspective	Temporary Staffing Services: A Data Mining Perspective	14:00 - 14:25	Temporary Staffing Services: A Data Mining Perspective Jeroen D'Haen and Dirk Van den Poel
14:25 - 14:50	An Analytics Approach for Proactively Combating Voluntary Attrition of Employees Moninder Singh, Kush Varshney, Jun Wang, Aleksandra Mojsilovic, Mark Squillante, Yingdong Lu, Alisia Gill, Patricia Faur and Raphael Ezry	14:25 - 14:50	An Analytics Approach for Proactively Combating Voluntary Attrition of Employees Moninder Singh, Kush Varshney, Jun Wang, Aleksandra Mojsilovic, Mark Squillante, Yingdong Lu, Alisia Gill, Patricia Faur and Raphael Ezry	14:25 - 14:50	An Analytics Approach for Proactively Combating Voluntary Attrition of Employees	An Analytics Approach for Proactively Combating Voluntary Attrition of Employees	14:25 - 14:50	An Analytics Approach for Proactively Combating Voluntary Attrition of Employees Moninder Singh, Kush Varshney, Jun Wang, Aleksandra Mojsilovic, Mark Squillante, Yingdong Lu, Alisia Gill, Patricia Faur and Raphael Ezry
14:50 - 15:15	Viewers' side analysis of social interests Takeshi Mitamura and Kenichi Yoshida	14:50 - 15:15	Viewers' side analysis of social interests Takeshi Mitamura and Kenichi Yoshida	14:50 - 15:15	Viewers' side analysis of social interests	Viewers' side analysis of social interests	14:50 - 15:15	Viewers' side analysis of social interests Takeshi Mitamura and Kenichi Yoshida
15:15 - 15:45	Coffee Break	15:15 - 15:45	Coffee Break	15:15 - 15:45	Coffee Break	Coffee Break	15:15 - 15:45	Coffee Break
15:45 - 17:25	Navigation and Service	15:45 - 17:25	Navigation and Service	15:45 - 17:25	Navigation and Service	Navigation and Service	15:45 - 17:25	Navigation and Service
15:45 - 16:10	Data-oriented Construction and Maintenance of Clinical Pathway using similarity-based data mining methods Haruko Iwata, Shoji Hirano and Shusaku Tsumoto	15:45 - 16:10	Data-oriented Construction and Maintenance of Clinical Pathway using similarity-based data mining methods Haruko Iwata, Shoji Hirano and Shusaku Tsumoto	15:45 - 16:10	Data-oriented Construction and Maintenance of Clinical Pathway using similarity-based data mining methods	Data-oriented Construction and Maintenance of Clinical Pathway using similarity-based data mining methods	15:45 - 16:10	Data-oriented Construction and Maintenance of Clinical Pathway using similarity-based data mining methods Haruko Iwata, Shoji Hirano and Shusaku Tsumoto
16:10 - 16:35	Applying an Auction Data Generator to the Evaluation of Fraud Detection Algorithms Sidney Tsang, Yun Sing Koh and Gillian Dobbie	16:10 - 16:35	Applying an Auction Data Generator to the Evaluation of Fraud Detection Algorithms Sidney Tsang, Yun Sing Koh and Gillian Dobbie	16:10 - 16:35	Applying an Auction Data Generator to the Evaluation of Fraud Detection Algorithms	Applying an Auction Data Generator to the Evaluation of Fraud Detection Algorithms	16:10 - 16:35	Applying an Auction Data Generator to the Evaluation of Fraud Detection Algorithms Sidney Tsang, Yun Sing Koh and Gillian Dobbie
16:35 - 17:00	Data Mining in the Age of Curation Akinori Abe	16:35 - 17:00	Data Mining in the Age of Curation Akinori Abe	16:35 - 17:00	Data Mining in the Age of Curation	Data Mining in the Age of Curation	16:35 - 17:00	Data Mining in the Age of Curation Akinori Abe
17:00 - 17:05	Closing Shusaku Tsumoto and Katsutoshi Yada	17:00 - 17:05	Closing Shusaku Tsumoto and Katsutoshi Yada	17:00 - 17:05	Closing	Closing	17:00 - 17:05	Closing Shusaku Tsumoto and Katsutoshi Yada

DPADM: IEEE ICDM 2012 International Workshop on Discrimination and Privacy-Aware Data Mining

Organized by Toon Calders and Indrė Žliobaitė
08:30 - 12:30
Room: Tintoretto II
https://sites.google.com/site/dpadm2012

The workshop focuses on technological, legal, ethical and social issues of discrimination and privacy in data mining.

08:30 - 08:40	Introduction	08:30 - 08:40	Introduction	08:30 - 08:40	Introduction	Introduction	08:30 - 08:40	Introduction
08:40 - 09:00	Discovering gender discrimination in project funding Andrea Romei, Salvatore Ruggieri and Franco Turini	08:40 - 09:00	Discovering gender discrimination in project funding Andrea Romei, Salvatore Ruggieri and Franco Turini	08:40 - 09:00	Discovering gender discrimination in project funding	Discovering gender discrimination in project funding	08:40 - 09:00	Discovering gender discrimination in project funding Andrea Romei, Salvatore Ruggieri and Franco Turini
09:00 - 09:20	Avoiding discrimination when classifying socially sensitive data Faisal Kamiran, Asim Karim, Sicco Verwer and Heike Goudriaan	09:00 - 09:20	Avoiding discrimination when classifying socially sensitive data Faisal Kamiran, Asim Karim, Sicco Verwer and Heike Goudriaan	09:00 - 09:20	Avoiding discrimination when classifying socially sensitive data	Avoiding discrimination when classifying socially sensitive data	09:00 - 09:20	Avoiding discrimination when classifying socially sensitive data Faisal Kamiran, Asim Karim, Sicco Verwer and Heike Goudriaan
09:20 - 09:40	Discriminatory Decision Policy Aware Classification Koray Mancuhan and Chris Clifton	09:20 - 09:40	Discriminatory Decision Policy Aware Classification Koray Mancuhan and Chris Clifton	09:20 - 09:40	Discriminatory Decision Policy Aware Classification	Discriminatory Decision Policy Aware Classification	09:20 - 09:40	Discriminatory Decision Policy Aware Classification Koray Mancuhan and Chris Clifton
09:40 - 10:00	Injecting Discrimination and Privacy Awareness into Pattern Discovery Sara Hajian, Anna Monreale, Dino Pedreschi, Josep Domingo-Ferrer and Fosca Giannotti	09:40 - 10:00	Injecting Discrimination and Privacy Awareness into Pattern Discovery Sara Hajian, Anna Monreale, Dino Pedreschi, Josep Domingo-Ferrer and Fosca Giannotti	09:40 - 10:00	Injecting Discrimination and Privacy Awareness into Pattern Discovery	Injecting Discrimination and Privacy Awareness into Pattern Discovery	09:40 - 10:00	Injecting Discrimination and Privacy Awareness into Pattern Discovery Sara Hajian, Anna Monreale, Dino Pedreschi, Josep Domingo-Ferrer and Fosca Giannotti
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 10:50	Exploring discrimination: A user-centric evaluation of discrimination-aware data mining Bettina Berendt and Soeren Preibusch	10:30 - 10:50	Exploring discrimination: A user-centric evaluation of discrimination-aware data mining Bettina Berendt and Soeren Preibusch	10:30 - 10:50	Exploring discrimination: A user-centric evaluation of discrimination-aware data mining	Exploring discrimination: A user-centric evaluation of discrimination-aware data mining	10:30 - 10:50	Exploring discrimination: A user-centric evaluation of discrimination-aware data mining Bettina Berendt and Soeren Preibusch
10:50 - 11:10	A Study on the Impact of Data Anonymization on Anti-discrimination Sara Hajian and Josep Domingo-Ferrer	10:50 - 11:10	A Study on the Impact of Data Anonymization on Anti-discrimination Sara Hajian and Josep Domingo-Ferrer	10:50 - 11:10	A Study on the Impact of Data Anonymization on Anti-discrimination	A Study on the Impact of Data Anonymization on Anti-discrimination	10:50 - 11:10	A Study on the Impact of Data Anonymization on Anti-discrimination Sara Hajian and Josep Domingo-Ferrer
11:10 - 11:30	Considerations on Fairness-aware Data Mining Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh and Jun Sakuma	11:10 - 11:30	Considerations on Fairness-aware Data Mining Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh and Jun Sakuma	11:10 - 11:30	Considerations on Fairness-aware Data Mining	Considerations on Fairness-aware Data Mining	11:10 - 11:30	Considerations on Fairness-aware Data Mining Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh and Jun Sakuma
11:30 - 12:15	Invited Talk: Challenges of Ambient Law and Legal Protection in the Profiling Era Prof. dr. Mireille Hildebrandt	11:30 - 12:15	Invited Talk: Challenges of Ambient Law and Legal Protection in the Profiling Era Prof. dr. Mireille Hildebrandt	11:30 - 12:15	Invited Talk: Challenges of Ambient Law and Legal Protection in the Profiling Era	Invited Talk: Challenges of Ambient Law and Legal Protection in the Profiling Era	11:30 - 12:15	Invited Talk: Challenges of Ambient Law and Legal Protection in the Profiling Era Prof. dr. Mireille Hildebrandt
12:15 - 12:30	Closing discussion	12:15 - 12:30	Closing discussion	12:15 - 12:30	Closing discussion	Closing discussion	12:15 - 12:30	Closing discussion

KDCloud: Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms

Organized by Ranga Raju Vatsavai and Sanjay Ranka
09:00 - 12:45
Room: Tintoretto I
http://www.ornl.gov/sci/knowledgediscovery/CloudComputing/KDCloud-12

This workshop features a keynote speaker and seven peer-reviewed technical presentations covering a broad specturm of cloud enabled data mining schemes. We welcome all data miners to attend this workshop.

09:00 - 09:10	Welcoming and Introduction Ranga Raju Vatsavai	09:00 - 09:10	Welcoming and Introduction Ranga Raju Vatsavai	09:00 - 09:10	Welcoming and Introduction	Welcoming and Introduction	09:00 - 09:10	Welcoming and Introduction Ranga Raju Vatsavai
09:10 - 09:50	Keynote Talk	09:10 - 09:50	Keynote Talk	09:10 - 09:50	Keynote Talk	Keynote Talk	09:10 - 09:50	Keynote Talk
09:50 - 10:10	Genetic Algorithm based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks Anand Kannan, Ayush Sharma, Gerald Q. Maguire, Jr., and Peter Schoo	09:50 - 10:10	Genetic Algorithm based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks Anand Kannan, Ayush Sharma, Gerald Q. Maguire, Jr., and Peter Schoo	09:50 - 10:10	Genetic Algorithm based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks	Genetic Algorithm based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks	09:50 - 10:10	Genetic Algorithm based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks Anand Kannan, Ayush Sharma, Gerald Q. Maguire, Jr., and Peter Schoo
10:10 - 10:30	MapReduce-based Closed Frequent Itemset Mining with Efficient Redundancy Filtering Su-Qi Wang, Yu-Bin Yang, Yang Gao, Guang-Peng Chen, and Yao Zhang	10:10 - 10:30	MapReduce-based Closed Frequent Itemset Mining with Efficient Redundancy Filtering Su-Qi Wang, Yu-Bin Yang, Yang Gao, Guang-Peng Chen, and Yao Zhang	10:10 - 10:30	MapReduce-based Closed Frequent Itemset Mining with Efficient Redundancy Filtering	MapReduce-based Closed Frequent Itemset Mining with Efficient Redundancy Filtering	10:10 - 10:30	MapReduce-based Closed Frequent Itemset Mining with Efficient Redundancy Filtering Su-Qi Wang, Yu-Bin Yang, Yang Gao, Guang-Peng Chen, and Yao Zhang
10:30 - 11:00	Coffee Break	10:30 - 11:00	Coffee Break	10:30 - 11:00	Coffee Break	Coffee Break	10:30 - 11:00	Coffee Break
11:00 - 11:20	Using Storm to Perform Dynamic Egocentric Network Motif Analysis Martin Harrigan, Lorcan Coyle, and Padraig Cunningham	11:00 - 11:20	Using Storm to Perform Dynamic Egocentric Network Motif Analysis Martin Harrigan, Lorcan Coyle, and Padraig Cunningham	11:00 - 11:20	Using Storm to Perform Dynamic Egocentric Network Motif Analysis	Using Storm to Perform Dynamic Egocentric Network Motif Analysis	11:00 - 11:20	Using Storm to Perform Dynamic Egocentric Network Motif Analysis Martin Harrigan, Lorcan Coyle, and Padraig Cunningham
11:20 - 11:40	Parallel Concept Drift Detection with Online Map-Reduce Artur Andrzejak and Joao Gomes	11:20 - 11:40	Parallel Concept Drift Detection with Online Map-Reduce Artur Andrzejak and Joao Gomes	11:20 - 11:40	Parallel Concept Drift Detection with Online Map-Reduce	Parallel Concept Drift Detection with Online Map-Reduce	11:20 - 11:40	Parallel Concept Drift Detection with Online Map-Reduce Artur Andrzejak and Joao Gomes
11:40 - 12:00	Convex-Concave Hull for Classification with Support Vector Machine Asdrubal López-Chau, Xiaoou Li, Wen Yu, and Jair Cervantes	11:40 - 12:00	Convex-Concave Hull for Classification with Support Vector Machine Asdrubal López-Chau, Xiaoou Li, Wen Yu, and Jair Cervantes	11:40 - 12:00	Convex-Concave Hull for Classification with Support Vector Machine	Convex-Concave Hull for Classification with Support Vector Machine	11:40 - 12:00	Convex-Concave Hull for Classification with Support Vector Machine Asdrubal López-Chau, Xiaoou Li, Wen Yu, and Jair Cervantes
12:00 - 12:20	Large Scale KNN-Graph Approximation Mohamed Riadh Trad, Alexis Joly, and Nozha Boujemaa	12:00 - 12:20	Large Scale KNN-Graph Approximation Mohamed Riadh Trad, Alexis Joly, and Nozha Boujemaa	12:00 - 12:20	Large Scale KNN-Graph Approximation	Large Scale KNN-Graph Approximation	12:00 - 12:20	Large Scale KNN-Graph Approximation Mohamed Riadh Trad, Alexis Joly, and Nozha Boujemaa
12:20 - 12:40	Scalable Clustering Using PACT Programming Model Sharanjit Kaur, Tripti Gupta, Dhriti khanna, and Vasudha Bhatnagar	12:20 - 12:40	Scalable Clustering Using PACT Programming Model Sharanjit Kaur, Tripti Gupta, Dhriti khanna, and Vasudha Bhatnagar	12:20 - 12:40	Scalable Clustering Using PACT Programming Model	Scalable Clustering Using PACT Programming Model	12:20 - 12:40	Scalable Clustering Using PACT Programming Model Sharanjit Kaur, Tripti Gupta, Dhriti khanna, and Vasudha Bhatnagar
12:40 - 12:45	Closing Remarks	12:40 - 12:45	Closing Remarks	12:40 - 12:45	Closing Remarks	Closing Remarks	12:40 - 12:45	Closing Remarks

OEDM: Workshop on Optimization Based Techniques for Emerging Data Mining

Organized by Yong Shi and Chris Ding
08:45 - 17:10
Room: Willumsen
http://www.feds.ac.cn/academic/Pages/academicShow.aspx?academicID=16

This workshop will present recent advances in optimization techniques for, especially new emerging, data mining problems, as well as the real-life applications among. One main goal of the workshop is to bring together the leading researchers who work on state-of-the-art algorithms on optimization based methods for modern data analysis, and also the practitioners who seek for novel applications.

08:45 - 09:10	Anomalous Neighborhood Selection Satoshi Hara and Takashi WASHIO	08:45 - 09:10	Anomalous Neighborhood Selection Satoshi Hara and Takashi WASHIO	08:45 - 09:10	Anomalous Neighborhood Selection	Anomalous Neighborhood Selection	08:45 - 09:10	Anomalous Neighborhood Selection Satoshi Hara and Takashi WASHIO
09:10 - 09:35	The Performance of alternative Exchange Rate Regimes and Their Countries condition: Matching Analysis and Selection Model Building Haizhen Yang, Yunpeng Song, and Kun Guo	09:10 - 09:35	The Performance of alternative Exchange Rate Regimes and Their Countries condition: Matching Analysis and Selection Model Building Haizhen Yang, Yunpeng Song, and Kun Guo	09:10 - 09:35	The Performance of alternative Exchange Rate Regimes and Their Countries condition: Matching Analysis and Selection Model Building	The Performance of alternative Exchange Rate Regimes and Their Countries condition: Matching Analysis and Selection Model Building	09:10 - 09:35	The Performance of alternative Exchange Rate Regimes and Their Countries condition: Matching Analysis and Selection Model Building Haizhen Yang, Yunpeng Song, and Kun Guo
09:35 - 10:00	Employing Principal Hessian Direction for Building Hinging Hyperplane Models Anca Maria Ivanescu, Thivaharan Albin, Dirk Abel, and Thomas Seidl	09:35 - 10:00	Employing Principal Hessian Direction for Building Hinging Hyperplane Models Anca Maria Ivanescu, Thivaharan Albin, Dirk Abel, and Thomas Seidl	09:35 - 10:00	Employing Principal Hessian Direction for Building Hinging Hyperplane Models	Employing Principal Hessian Direction for Building Hinging Hyperplane Models	09:35 - 10:00	Employing Principal Hessian Direction for Building Hinging Hyperplane Models Anca Maria Ivanescu, Thivaharan Albin, Dirk Abel, and Thomas Seidl
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 10:55	Nonlinear L-1 Support Vector Machines for Learning Using Privileged Information Lingfeng Niu, and Jianmin Wu	10:30 - 10:55	Nonlinear L-1 Support Vector Machines for Learning Using Privileged Information Lingfeng Niu, and Jianmin Wu	10:30 - 10:55	Nonlinear L-1 Support Vector Machines for Learning Using Privileged Information	Nonlinear L-1 Support Vector Machines for Learning Using Privileged Information	10:30 - 10:55	Nonlinear L-1 Support Vector Machines for Learning Using Privileged Information Lingfeng Niu, and Jianmin Wu
10:55 - 11:20	The Transfer Learning Based on Relationships between Attributes Jinwei Zhao, Boqin Feng, Guirong Yan, and Longlei Dong	10:55 - 11:20	The Transfer Learning Based on Relationships between Attributes Jinwei Zhao, Boqin Feng, Guirong Yan, and Longlei Dong	10:55 - 11:20	The Transfer Learning Based on Relationships between Attributes	The Transfer Learning Based on Relationships between Attributes	10:55 - 11:20	The Transfer Learning Based on Relationships between Attributes Jinwei Zhao, Boqin Feng, Guirong Yan, and Longlei Dong
11:20 - 11:45	Overlapping Clustering with Sparseness Constraints Haibing Lu, Yuan Hong, Nick Street, Fei Wang, and Hanghang Tong	11:20 - 11:45	Overlapping Clustering with Sparseness Constraints Haibing Lu, Yuan Hong, Nick Street, Fei Wang, and Hanghang Tong	11:20 - 11:45	Overlapping Clustering with Sparseness Constraints	Overlapping Clustering with Sparseness Constraints	11:20 - 11:45	Overlapping Clustering with Sparseness Constraints Haibing Lu, Yuan Hong, Nick Street, Fei Wang, and Hanghang Tong
11:45 - 13:30	Lunch Break	11:45 - 13:30	Lunch Break	11:45 - 13:30	Lunch Break	Lunch Break	11:45 - 13:30	Lunch Break
13:30 - 14:30	Invited Report Prof. Lieven De Lathauwer	13:30 - 14:30	Invited Report Prof. Lieven De Lathauwer	13:30 - 14:30	Invited Report	Invited Report	13:30 - 14:30	Invited Report Prof. Lieven De Lathauwer
14:30 - 14:50	Coffee Break	14:30 - 14:50	Coffee Break	14:30 - 14:50	Coffee Break	Coffee Break	14:30 - 14:50	Coffee Break
14:50 - 15:10	Rare Events Forecasting Using a Residual-Feedback GMDH Neural Network Simon Fong	14:50 - 15:10	Rare Events Forecasting Using a Residual-Feedback GMDH Neural Network Simon Fong	14:50 - 15:10	Rare Events Forecasting Using a Residual-Feedback GMDH Neural Network	Rare Events Forecasting Using a Residual-Feedback GMDH Neural Network	14:50 - 15:10	Rare Events Forecasting Using a Residual-Feedback GMDH Neural Network Simon Fong
15:10 - 15:30	OCCAMS - An Optimal Combinatorial Covering Algorithm for Multi-document Summarization Sashka Davis, Conroy John, and Judith Schlesinger	15:10 - 15:30	OCCAMS - An Optimal Combinatorial Covering Algorithm for Multi-document Summarization Sashka Davis, Conroy John, and Judith Schlesinger	15:10 - 15:30	OCCAMS - An Optimal Combinatorial Covering Algorithm for Multi-document Summarization	OCCAMS - An Optimal Combinatorial Covering Algorithm for Multi-document Summarization	15:10 - 15:30	OCCAMS - An Optimal Combinatorial Covering Algorithm for Multi-document Summarization Sashka Davis, Conroy John, and Judith Schlesinger
15:30 - 15:50	Learning from multiple annotators : when data is hard and annotators are unreliable Chirine Wolley and Mohamed Quafafou	15:30 - 15:50	Learning from multiple annotators : when data is hard and annotators are unreliable Chirine Wolley and Mohamed Quafafou	15:30 - 15:50	Learning from multiple annotators : when data is hard and annotators are unreliable	Learning from multiple annotators : when data is hard and annotators are unreliable	15:30 - 15:50	Learning from multiple annotators : when data is hard and annotators are unreliable Chirine Wolley and Mohamed Quafafou
15:50 - 16:10	Coffee Break	15:50 - 16:10	Coffee Break	15:50 - 16:10	Coffee Break	Coffee Break	15:50 - 16:10	Coffee Break
16:10 - 16:30	Nonlinear Unsupervised Feature Learning: How Local Similarities Lead to Global Coding Amirreza Shaban, Hamid R. Rabiee, Marzieh S. Tahaei, and Erfan Salavati	16:10 - 16:30	Nonlinear Unsupervised Feature Learning: How Local Similarities Lead to Global Coding Amirreza Shaban, Hamid R. Rabiee, Marzieh S. Tahaei, and Erfan Salavati	16:10 - 16:30	Nonlinear Unsupervised Feature Learning: How Local Similarities Lead to Global Coding	Nonlinear Unsupervised Feature Learning: How Local Similarities Lead to Global Coding	16:10 - 16:30	Nonlinear Unsupervised Feature Learning: How Local Similarities Lead to Global Coding Amirreza Shaban, Hamid R. Rabiee, Marzieh S. Tahaei, and Erfan Salavati
16:30 - 16:50	Robust Kernel Nonnegative Matrix Factorization Zhichen Xia, Chris Ding, and Edmond Chow	16:30 - 16:50	Robust Kernel Nonnegative Matrix Factorization Zhichen Xia, Chris Ding, and Edmond Chow	16:30 - 16:50	Robust Kernel Nonnegative Matrix Factorization	Robust Kernel Nonnegative Matrix Factorization	16:30 - 16:50	Robust Kernel Nonnegative Matrix Factorization Zhichen Xia, Chris Ding, and Edmond Chow
16:50 - 17:10	Regular Multiple Criteria Linear Programming for Semi-supervised Classification Zhiquan Qi, Yingjie Tian, and Yong Shi	16:50 - 17:10	Regular Multiple Criteria Linear Programming for Semi-supervised Classification Zhiquan Qi, Yingjie Tian, and Yong Shi	16:50 - 17:10	Regular Multiple Criteria Linear Programming for Semi-supervised Classification	Regular Multiple Criteria Linear Programming for Semi-supervised Classification	16:50 - 17:10	Regular Multiple Criteria Linear Programming for Semi-supervised Classification Zhiquan Qi, Yingjie Tian, and Yong Shi

PhD Forum - ICDM 2012

Organized by Yann-Aël Le Borgne and Evimaria Terzi
09:00 - 18:00
Room: Salle des Nation II
http://icdm2012.ua.ac.be/content/phd-forum

The aim of the Forum is to provide an international environment in which students can meet and exchange their ideas and experiences both with peers and with senior researchers from the Data Mining Community. The Forum is particularly aimed at PhD students in the early stages of their career and Master's students planning to pursue their research in a PhD programme. The session will be chaired by Yann-Aël Le Borgne and Patrick Meyer.

09:00 - 09:10	Welcome and Introduction Yann-Aël Le Borgne	09:00 - 09:10	Welcome and Introduction Yann-Aël Le Borgne	09:00 - 09:10	Welcome and Introduction	Welcome and Introduction	09:00 - 09:10	Welcome and Introduction Yann-Aël Le Borgne
09:10 - 10:00	Invited Talk: Several diverse mining problems (and publications) with the same input data: an example with propagation data in social networks Francesco Bonchi	09:10 - 10:00	Invited Talk: Several diverse mining problems (and publications) with the same input data: an example with propagation data in social networks Francesco Bonchi	09:10 - 10:00	Invited Talk: Several diverse mining problems (and publications) with the same input data: an example with propagation data in social networks	Invited Talk: Several diverse mining problems (and publications) with the same input data: an example with propagation data in social networks	09:10 - 10:00	Invited Talk: Several diverse mining problems (and publications) with the same input data: an example with propagation data in social networks Francesco Bonchi
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 10:50	Modeling of Collective Synchronous Behavior on Social Media Victor C. Liang and Vincent T.Y. Ng	10:30 - 10:50	Modeling of Collective Synchronous Behavior on Social Media Victor C. Liang and Vincent T.Y. Ng	10:30 - 10:50	Modeling of Collective Synchronous Behavior on Social Media	Modeling of Collective Synchronous Behavior on Social Media	10:30 - 10:50	Modeling of Collective Synchronous Behavior on Social Media Victor C. Liang and Vincent T.Y. Ng
10:50 - 11:10	Selecting accurate and comprehensible regression algorithms through meta learning Gert Loterman and Christophe Mues	10:50 - 11:10	Selecting accurate and comprehensible regression algorithms through meta learning Gert Loterman and Christophe Mues	10:50 - 11:10	Selecting accurate and comprehensible regression algorithms through meta learning	Selecting accurate and comprehensible regression algorithms through meta learning	10:50 - 11:10	Selecting accurate and comprehensible regression algorithms through meta learning Gert Loterman and Christophe Mues
11:10 - 11:30	Multi-slice Modularity Optimization in Community Detection and Image segmentation Huiyi Hu, Yves van Gennip, Blake Hunter, Andrea L. Bertozzi and Mason A. Porter	11:10 - 11:30	Multi-slice Modularity Optimization in Community Detection and Image segmentation Huiyi Hu, Yves van Gennip, Blake Hunter, Andrea L. Bertozzi and Mason A. Porter	11:10 - 11:30	Multi-slice Modularity Optimization in Community Detection and Image segmentation	Multi-slice Modularity Optimization in Community Detection and Image segmentation	11:10 - 11:30	Multi-slice Modularity Optimization in Community Detection and Image segmentation Huiyi Hu, Yves van Gennip, Blake Hunter, Andrea L. Bertozzi and Mason A. Porter
11:30 - 11:50	Sorted Neighborhoods for Multidimensional Privacy-Preserving Blocking Alexandros Karakasidis and Vassilios S. Verykios	11:30 - 11:50	Sorted Neighborhoods for Multidimensional Privacy-Preserving Blocking Alexandros Karakasidis and Vassilios S. Verykios	11:30 - 11:50	Sorted Neighborhoods for Multidimensional Privacy-Preserving Blocking	Sorted Neighborhoods for Multidimensional Privacy-Preserving Blocking	11:30 - 11:50	Sorted Neighborhoods for Multidimensional Privacy-Preserving Blocking Alexandros Karakasidis and Vassilios S. Verykios
11:50 - 12:10	Effective Text Classification by a Supervised Feature Selection Approach Tanmay Basu and C. A. Murthy	11:50 - 12:10	Effective Text Classification by a Supervised Feature Selection Approach Tanmay Basu and C. A. Murthy	11:50 - 12:10	Effective Text Classification by a Supervised Feature Selection Approach	Effective Text Classification by a Supervised Feature Selection Approach	11:50 - 12:10	Effective Text Classification by a Supervised Feature Selection Approach Tanmay Basu and C. A. Murthy
12:10 - 12:30	Active Learning based Rule Extraction for Regression Enric Junqué de Fortuny and David Martens	12:10 - 12:30	Active Learning based Rule Extraction for Regression Enric Junqué de Fortuny and David Martens	12:10 - 12:30	Active Learning based Rule Extraction for Regression	Active Learning based Rule Extraction for Regression	12:10 - 12:30	Active Learning based Rule Extraction for Regression Enric Junqué de Fortuny and David Martens
12:30 - 14:00	Lunch Break	12:30 - 14:00	Lunch Break	12:30 - 14:00	Lunch Break	Lunch Break	12:30 - 14:00	Lunch Break
14:00 - 14:50	Invited Talk: How to Make an Effective Presentation Francois-Xavier Willems	14:00 - 14:50	Invited Talk: How to Make an Effective Presentation Francois-Xavier Willems	14:00 - 14:50	Invited Talk: How to Make an Effective Presentation	Invited Talk: How to Make an Effective Presentation	14:00 - 14:50	Invited Talk: How to Make an Effective Presentation Francois-Xavier Willems
14:50 - 15:10	Imputation of HLA genes from SNP data Vanja Paunić, Michael Steinbach, Vipin Kumar and Martin Maiers	14:50 - 15:10	Imputation of HLA genes from SNP data Vanja Paunić, Michael Steinbach, Vipin Kumar and Martin Maiers	14:50 - 15:10	Imputation of HLA genes from SNP data	Imputation of HLA genes from SNP data	14:50 - 15:10	Imputation of HLA genes from SNP data Vanja Paunić, Michael Steinbach, Vipin Kumar and Martin Maiers
15:10 - 15:30	Towards a Particle Swarm Optimization-Based Regression Rule Miner Bart Minnaert and David Martens	15:10 - 15:30	Towards a Particle Swarm Optimization-Based Regression Rule Miner Bart Minnaert and David Martens	15:10 - 15:30	Towards a Particle Swarm Optimization-Based Regression Rule Miner	Towards a Particle Swarm Optimization-Based Regression Rule Miner	15:10 - 15:30	Towards a Particle Swarm Optimization-Based Regression Rule Miner Bart Minnaert and David Martens
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 17:00	Poster session	16:00 - 17:00	Poster session	16:00 - 17:00	Poster session	Poster session	16:00 - 17:00	Poster session
17:00 - 18:00	Conclusions	17:00 - 18:00	Conclusions	17:00 - 18:00	Conclusions	Conclusions	17:00 - 18:00	Conclusions

PinSoDa: Workshop on Privacy in Social Data

Organized by Anna Monreale, Dino Pedreschi, Chedy Raissi and Yucel Saygin
14:00 - 18:00
Room: Tintoretto II
http://kdd.isti.cnr.it/pinsoda

One of the most fascinating challenges of our time is to understand the complexity of the global interconnected society we inhabit. Social data analysis can help us understand complex social phenomena, such as mobility, relationships and social connections, economic trends, spread of epidemics, opinion diffusion, sustainability and so on.

The opportunities of discovering knowledge from social data increase with the risk of privacy violation.

Privacy intrusion jeopardizes trust: if not adequately countered, they can undermine the idea of a fair and democratic knowledge society.

14:00 - 14:15	Workshop Opening	14:00 - 14:15	Workshop Opening	14:00 - 14:15	Workshop Opening	Workshop Opening	14:00 - 14:15	Workshop Opening
14:15 - 15:00	Invited Talk: Discrimination data analysis and its relations with privacy Salvatore Ruggieri	14:15 - 15:00	Invited Talk: Discrimination data analysis and its relations with privacy Salvatore Ruggieri	14:15 - 15:00	Invited Talk: Discrimination data analysis and its relations with privacy	Invited Talk: Discrimination data analysis and its relations with privacy	14:15 - 15:00	Invited Talk: Discrimination data analysis and its relations with privacy Salvatore Ruggieri
15:00 - 15:30	A Practical System for Privacy-Preserving Collaborative Filtering Richard Chow, Manas Pathak, and Cong Wang	15:00 - 15:30	A Practical System for Privacy-Preserving Collaborative Filtering Richard Chow, Manas Pathak, and Cong Wang	15:00 - 15:30	A Practical System for Privacy-Preserving Collaborative Filtering	A Practical System for Privacy-Preserving Collaborative Filtering	15:00 - 15:30	A Practical System for Privacy-Preserving Collaborative Filtering Richard Chow, Manas Pathak, and Cong Wang
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 16:30	Exploiting Dynamic Privacy in Socially Regularized Recommenders Ramona Bunea, Shahab Mokarizadeh, Nima Dokoohaki, and Mihhail Matski	16:00 - 16:30	Exploiting Dynamic Privacy in Socially Regularized Recommenders Ramona Bunea, Shahab Mokarizadeh, Nima Dokoohaki, and Mihhail Matski	16:00 - 16:30	Exploiting Dynamic Privacy in Socially Regularized Recommenders	Exploiting Dynamic Privacy in Socially Regularized Recommenders	16:00 - 16:30	Exploiting Dynamic Privacy in Socially Regularized Recommenders Ramona Bunea, Shahab Mokarizadeh, Nima Dokoohaki, and Mihhail Matski
16:30 - 17:00	Beware of What You Share: Inferring Home Location in Social Networks Tatiana Pontes, Gabriel Magno, Marisa Vasconcelos, Aditi Gupta, Jussara Almeida, Ponnurangam Kumaraguru, and Virgilio Almeida	16:30 - 17:00	Beware of What You Share: Inferring Home Location in Social Networks Tatiana Pontes, Gabriel Magno, Marisa Vasconcelos, Aditi Gupta, Jussara Almeida, Ponnurangam Kumaraguru, and Virgilio Almeida	16:30 - 17:00	Beware of What You Share: Inferring Home Location in Social Networks	Beware of What You Share: Inferring Home Location in Social Networks	16:30 - 17:00	Beware of What You Share: Inferring Home Location in Social Networks Tatiana Pontes, Gabriel Magno, Marisa Vasconcelos, Aditi Gupta, Jussara Almeida, Ponnurangam Kumaraguru, and Virgilio Almeida
17:00 - 17:30	Interactive Grouping of Friends in OSN: Towards Online Context Management Bo Gao, Bettina Berendt, Dave Clarke, Ralf De Wolf, Thomas Peetz, Jo Pierson, and Rula Sayaf	17:00 - 17:30	Interactive Grouping of Friends in OSN: Towards Online Context Management Bo Gao, Bettina Berendt, Dave Clarke, Ralf De Wolf, Thomas Peetz, Jo Pierson, and Rula Sayaf	17:00 - 17:30	Interactive Grouping of Friends in OSN: Towards Online Context Management	Interactive Grouping of Friends in OSN: Towards Online Context Management	17:00 - 17:30	Interactive Grouping of Friends in OSN: Towards Online Context Management Bo Gao, Bettina Berendt, Dave Clarke, Ralf De Wolf, Thomas Peetz, Jo Pierson, and Rula Sayaf
17:30 - 18:00	Measuring Local Topological Anonymity in Social Networks Gábor György Gulyás and Sándor Imre	17:30 - 18:00	Measuring Local Topological Anonymity in Social Networks Gábor György Gulyás and Sándor Imre	17:30 - 18:00	Measuring Local Topological Anonymity in Social Networks	Measuring Local Topological Anonymity in Social Networks	17:30 - 18:00	Measuring Local Topological Anonymity in Social Networks Gábor György Gulyás and Sándor Imre

PTDM: Workshop on Practical Theories of Data Mining

Organized by Tijl De Bie, Kleanthis-Nikolaos (Akis) Kontonasios and Eirini Spyropoulou
08:30 - 18:00
Room: Rembrandt Room
https://sites.google.com/site/ptdm2012

The goal of this ICDM 2012 workshop is to help closing the gap between data mining practice and theory. To this end, we intend to explore what is the essence of exploratory data mining and how to formalize it in a useful but theoretically well-founded way.

The workshop is motivated by a widely perceived discrepancy between theoretical data mining prototypes and practitioners' requirements. A notable example is frequent pattern mining. Despite its attractive theoretical foundations, the practical use of frequent pattern mining methods has been limited. This is due to a difficulty to overcome issues, such as the pattern explosion problem and a discrepancy between usefulness and frequency. These issues have been addressed to some extent in the past 15 years, through heuristic post-processing steps and through rigorously motivated adaptations. The multitude of possible solution strategies has unfortunately to a large extent undermined the original elegance, and made it hard for practitioners to understand how to use these techniques.

The problem is however not restricted to frequent pattern mining alone. The multitude of available methods for typical exploratory data mining problems such as (subspace) clustering and dimensionality reduction is such that practitioners face a daunting task in selecting a suitable method. Additionally to the usability issues, less attention has been given on pattern mining methods for relational databases. Although most real world databases are relational, most pattern mining research has focused on one-table data.

We believe the core reasons for these difficulties are:

Different users inevitably have different prior beliefs and goals, whereas most exploratory data mining algorithms have a rigid objective function and do not consider this.
Formally comparing the quality of different data mining patterns is hard due to their widely varying nature (e.g. comparing a dimensionality reduction with a frequent itemset), unless their "interestingness" can be quantified in a comparable manner.
The iterative process of data mining is often not considered.
Data mining in complex relational data is hard to fit into standard data mining prototypes.
More generally, data mining methods tend to be rigid, defined for highly specific tasks, for highly specific and idealized data, and for very specific types of patterns./li>

The purpose of this workshop will be to serve as a forum of exchanging ideas on how to formalize exploratory data mining in order to make it useful in practice. This workshop will survey some existing attempts at addressing the problems mentioned above. We particularly encourage papers that present principled theoretical contributions motivated by real world requirements.

08:30 - 09:00	Introduction Tijl De Bie, Akis Kontonasios and Eirini Spyropoulou	08:30 - 09:00	Introduction Tijl De Bie, Akis Kontonasios and Eirini Spyropoulou	08:30 - 09:00	Introduction	Introduction	08:30 - 09:00	Introduction Tijl De Bie, Akis Kontonasios and Eirini Spyropoulou
09:00 - 10:00	Keynote Talk Kathleen Marchal, Universiteit Gent, Belgium	09:00 - 10:00	Keynote Talk Kathleen Marchal, Universiteit Gent, Belgium	09:00 - 10:00	Keynote Talk	Keynote Talk	09:00 - 10:00	Keynote Talk Kathleen Marchal, Universiteit Gent, Belgium
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 11:30	Keynote Talk: From Inductive Querying to Declarative Modelling for Data Mining Luc De Raedt, KU Leuven, Belgium	10:30 - 11:30	Keynote Talk: From Inductive Querying to Declarative Modelling for Data Mining Luc De Raedt, KU Leuven, Belgium	10:30 - 11:30	Keynote Talk: From Inductive Querying to Declarative Modelling for Data Mining	Keynote Talk: From Inductive Querying to Declarative Modelling for Data Mining	10:30 - 11:30	Keynote Talk: From Inductive Querying to Declarative Modelling for Data Mining Luc De Raedt, KU Leuven, Belgium
11:30 - 11:50	Thorough analysis of log data with dependency rules: Practical solutions and theoretical challenges Wilhelmiina Hämäläinen	11:30 - 11:50	Thorough analysis of log data with dependency rules: Practical solutions and theoretical challenges Wilhelmiina Hämäläinen	11:30 - 11:50	Thorough analysis of log data with dependency rules: Practical solutions and theoretical challenges	Thorough analysis of log data with dependency rules: Practical solutions and theoretical challenges	11:30 - 11:50	Thorough analysis of log data with dependency rules: Practical solutions and theoretical challenges Wilhelmiina Hämäläinen
11:50 - 12:10	Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner Christiane Kamdem Kengne, Leon Constantin Fopa, Noha Ibrahim, Alexandre Termier, Marie-Christine Rousset and Takashi Washio	11:50 - 12:10	Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner Christiane Kamdem Kengne, Leon Constantin Fopa, Noha Ibrahim, Alexandre Termier, Marie-Christine Rousset and Takashi Washio	11:50 - 12:10	Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner	Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner	11:50 - 12:10	Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner Christiane Kamdem Kengne, Leon Constantin Fopa, Noha Ibrahim, Alexandre Termier, Marie-Christine Rousset and Takashi Washio
12:10 - 12:30	Generalized Expansion Dimension Michael Nett, Michael E. Houle, and Hisashi Kashima	12:10 - 12:30	Generalized Expansion Dimension Michael Nett, Michael E. Houle, and Hisashi Kashima	12:10 - 12:30	Generalized Expansion Dimension	Generalized Expansion Dimension	12:10 - 12:30	Generalized Expansion Dimension Michael Nett, Michael E. Houle, and Hisashi Kashima
12:30 - 14:00	Lunch Break	12:30 - 14:00	Lunch Break	12:30 - 14:00	Lunch Break	Lunch Break	12:30 - 14:00	Lunch Break
14:00 - 15:00	Keynote Talk Kai Puolamäki, Aalto University, Finland	14:00 - 15:00	Keynote Talk Kai Puolamäki, Aalto University, Finland	14:00 - 15:00	Keynote Talk	Keynote Talk	14:00 - 15:00	Keynote Talk Kai Puolamäki, Aalto University, Finland
15:00 - 15:20	Generating Diverse Realistic Data Sets for Episode Mining Albrecht Zimmermann	15:00 - 15:20	Generating Diverse Realistic Data Sets for Episode Mining Albrecht Zimmermann	15:00 - 15:20	Generating Diverse Realistic Data Sets for Episode Mining	Generating Diverse Realistic Data Sets for Episode Mining	15:00 - 15:20	Generating Diverse Realistic Data Sets for Episode Mining Albrecht Zimmermann
15:20 - 15:40	Logical Itemset Mining Shailesh Kumar, Chandrashekar V. and C.V. Jawahar	15:20 - 15:40	Logical Itemset Mining Shailesh Kumar, Chandrashekar V. and C.V. Jawahar	15:20 - 15:40	Logical Itemset Mining	Logical Itemset Mining	15:20 - 15:40	Logical Itemset Mining Shailesh Kumar, Chandrashekar V. and C.V. Jawahar
15:40 - 16:00	Coffee Break	15:40 - 16:00	Coffee Break	15:40 - 16:00	Coffee Break	Coffee Break	15:40 - 16:00	Coffee Break
16:00 - 17:00	Keynote Talk Pieter Adriaans, Universiteit van Amsterdam, The Netherlands	16:00 - 17:00	Keynote Talk Pieter Adriaans, Universiteit van Amsterdam, The Netherlands	16:00 - 17:00	Keynote Talk	Keynote Talk	16:00 - 17:00	Keynote Talk Pieter Adriaans, Universiteit van Amsterdam, The Netherlands
17:00 - 18:00	Panel discussion TBC	17:00 - 18:00	Panel discussion TBC	17:00 - 18:00	Panel discussion	Panel discussion	17:00 - 18:00	Panel discussion TBC

RIKD: Workshop on Reliability Issues in Knowledge Discovery

Organized by Honghua Dai, James Liu, and Evgueni Smirnov
08:30 - 11:50
Room: Turner
http://www.deakin.edu.au/individuals-sites/?request=~hdai/RIKD12

The 2012 IEEE ICDM workshop ``Reliability Issues in Knowledge Discovery' aims at presenting the recent advances in the emerging field of reliable knowledge discovery from data. This year the workshop focus has shifted from theory and methods towards experimental studies and applications. The latter can be seen in the program consisting of 6 papers and an invited talk.

08:30 - 08:40	Welcoming and Introduction Honghua Dai and Evgueni Smirnov	08:30 - 08:40	Welcoming and Introduction Honghua Dai and Evgueni Smirnov	08:30 - 08:40	Welcoming and Introduction	Welcoming and Introduction	08:30 - 08:40	Welcoming and Introduction Honghua Dai and Evgueni Smirnov
08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning Georgi Nalbantov	08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning Georgi Nalbantov	08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning	08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning Georgi Nalbantov
09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation Yanxing Hu, James N. K. Liu, Yuan Wang and Lucas Lai	09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation Yanxing Hu, James N. K. Liu, Yuan Wang and Lucas Lai	09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation	09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation Yanxing Hu, James N. K. Liu, Yuan Wang and Lucas Lai
09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, and Tatsuya Tashiro	09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, and Tatsuya Tashiro	09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM	Bootstrap Confidence Intervals in DirectLiNGAM	09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, and Tatsuya Tashiro
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer Honghua Dai, Sarah Johnston, and Min Gan	10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer Honghua Dai, Sarah Johnston, and Min Gan	10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer	Reliable Knowledge Discovery with A Minimal Causal Model Inducer	10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer Honghua Dai, Sarah Johnston, and Min Gan
10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data Daniel Antwi, Herna Viktor, and Nathalie Japkowicz	10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data Daniel Antwi, Herna Viktor, and Nathalie Japkowicz	10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data	10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data Daniel Antwi, Herna Viktor, and Nathalie Japkowicz
11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification Abdul Nurunnabi and Geoff West	11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification Abdul Nurunnabi and Geoff West	11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification	11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification Abdul Nurunnabi and Geoff West
11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals Darko Pevec and Igor Kononenko	11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals Darko Pevec and Igor Kononenko	11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals	Model Selection with Combining Valid and Optimal Prediction Intervals	11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals Darko Pevec and Igor Kononenko

SENTIRE: Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction

Organized by Erik Cambria, Bing Liu, Yunqing Xia, Catherine Havasi
09:00 - 18:00
Room: Memling
http://sentic.net/sentire

Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE) is the IEEE ICDM workshop series on opinion mining. The term SENTIRE comes from the Latin feel and it is root of words such as sentiment and sensation. The main aim of SENTIRE is to explore the new frontiers of opinion mining and sentiment analysis by proposing novel techniques in fields such as AI, Semantic Web, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.

09:00 - 09:10	Welcoming and Introduction Erik Cambria	09:00 - 09:10	Welcoming and Introduction Erik Cambria	09:00 - 09:10	Welcoming and Introduction	Welcoming and Introduction	09:00 - 09:10	Welcoming and Introduction Erik Cambria
09:10 - 10:00	Keynote Talk: Multimodal Sentiment Analysis Rada Mihalcea	09:10 - 10:00	Keynote Talk: Multimodal Sentiment Analysis Rada Mihalcea	09:10 - 10:00	Keynote Talk: Multimodal Sentiment Analysis	Keynote Talk: Multimodal Sentiment Analysis	09:10 - 10:00	Keynote Talk: Multimodal Sentiment Analysis Rada Mihalcea
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 11:00	How Much Supervision? Corpus-Based Lexeme Sentiment Estimation Aleksander Wawer and Dominika Rogozinska	10:30 - 11:00	How Much Supervision? Corpus-Based Lexeme Sentiment Estimation Aleksander Wawer and Dominika Rogozinska	10:30 - 11:00	How Much Supervision? Corpus-Based Lexeme Sentiment Estimation	How Much Supervision? Corpus-Based Lexeme Sentiment Estimation	10:30 - 11:00	How Much Supervision? Corpus-Based Lexeme Sentiment Estimation Aleksander Wawer and Dominika Rogozinska
11:00 - 11:30	Domain Adaptation using Domain Similarity- and Domain Complexity-based Instance Selection Robert Remus	11:00 - 11:30	Domain Adaptation using Domain Similarity- and Domain Complexity-based Instance Selection Robert Remus	11:00 - 11:30	Domain Adaptation using Domain Similarity- and Domain Complexity-based Instance Selection	Domain Adaptation using Domain Similarity- and Domain Complexity-based Instance Selection	11:00 - 11:30	Domain Adaptation using Domain Similarity- and Domain Complexity-based Instance Selection Robert Remus
11:30 - 12:00	Sentiment polarity classification using statistical data compression models Dominique Ziegelmayer and Rainer Schrader	11:30 - 12:00	Sentiment polarity classification using statistical data compression models Dominique Ziegelmayer and Rainer Schrader	11:30 - 12:00	Sentiment polarity classification using statistical data compression models	Sentiment polarity classification using statistical data compression models	11:30 - 12:00	Sentiment polarity classification using statistical data compression models Dominique Ziegelmayer and Rainer Schrader
12:00 - 13:30	Lunch Break	12:00 - 13:30	Lunch Break	12:00 - 13:30	Lunch Break	Lunch Break	12:00 - 13:30	Lunch Break
13:30 - 14:00	Representing and Resolving Negation for Sentiment Analysis Emanuele Lapponi, Jonathon Read, and Lilja Ovrelid	13:30 - 14:00	Representing and Resolving Negation for Sentiment Analysis Emanuele Lapponi, Jonathon Read, and Lilja Ovrelid	13:30 - 14:00	Representing and Resolving Negation for Sentiment Analysis	Representing and Resolving Negation for Sentiment Analysis	13:30 - 14:00	Representing and Resolving Negation for Sentiment Analysis Emanuele Lapponi, Jonathon Read, and Lilja Ovrelid
14:00 - 14:30	Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining Sheng Huang, Xinlan Liu, Xueping Peng, and Zhendong Niu	14:00 - 14:30	Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining Sheng Huang, Xinlan Liu, Xueping Peng, and Zhendong Niu	14:00 - 14:30	Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining	Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining	14:00 - 14:30	Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining Sheng Huang, Xinlan Liu, Xueping Peng, and Zhendong Niu
14:30 - 15:00	Subjectivity-Based Features for Sentiment Classification: A Study on Two Lexicons Rahim Dehkharghani, Berrin Yanikoglu, Dilek Tapucu, and Yucel Saygin	14:30 - 15:00	Subjectivity-Based Features for Sentiment Classification: A Study on Two Lexicons Rahim Dehkharghani, Berrin Yanikoglu, Dilek Tapucu, and Yucel Saygin	14:30 - 15:00	Subjectivity-Based Features for Sentiment Classification: A Study on Two Lexicons	Subjectivity-Based Features for Sentiment Classification: A Study on Two Lexicons	14:30 - 15:00	Subjectivity-Based Features for Sentiment Classification: A Study on Two Lexicons Rahim Dehkharghani, Berrin Yanikoglu, Dilek Tapucu, and Yucel Saygin
15:00 - 15:30	Learning Domain-Specific Polarity Lexicons Gulsen Demiroz, Berrin Yanikoglu, Dilek Tapucu, and Yucel Saygin	15:00 - 15:30	Learning Domain-Specific Polarity Lexicons Gulsen Demiroz, Berrin Yanikoglu, Dilek Tapucu, and Yucel Saygin	15:00 - 15:30	Learning Domain-Specific Polarity Lexicons	Learning Domain-Specific Polarity Lexicons	15:00 - 15:30	Learning Domain-Specific Polarity Lexicons Gulsen Demiroz, Berrin Yanikoglu, Dilek Tapucu, and Yucel Saygin
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 16:30	A Regularized Recommendation Algorithm with Probabilistic Sentiment-Ratings Filipa Peleja, Pedro Dias, and Joao Magalhaes	16:00 - 16:30	A Regularized Recommendation Algorithm with Probabilistic Sentiment-Ratings Filipa Peleja, Pedro Dias, and Joao Magalhaes	16:00 - 16:30	A Regularized Recommendation Algorithm with Probabilistic Sentiment-Ratings	A Regularized Recommendation Algorithm with Probabilistic Sentiment-Ratings	16:00 - 16:30	A Regularized Recommendation Algorithm with Probabilistic Sentiment-Ratings Filipa Peleja, Pedro Dias, and Joao Magalhaes
16:30 - 17:00	Enriching SenticNet Polarity Scores Through Semi-Supervised Fuzzy Clustering Soujanya Poria, Alexandar Gelbukh, Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay	16:30 - 17:00	Enriching SenticNet Polarity Scores Through Semi-Supervised Fuzzy Clustering Soujanya Poria, Alexandar Gelbukh, Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay	16:30 - 17:00	Enriching SenticNet Polarity Scores Through Semi-Supervised Fuzzy Clustering	Enriching SenticNet Polarity Scores Through Semi-Supervised Fuzzy Clustering	16:30 - 17:00	Enriching SenticNet Polarity Scores Through Semi-Supervised Fuzzy Clustering Soujanya Poria, Alexandar Gelbukh, Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay
17:00 - 17:30	Full Spectrum Opinion Mining: Integrating Domain, Syntactic and Lexical Knowledge Daniel Olsher	17:00 - 17:30	Full Spectrum Opinion Mining: Integrating Domain, Syntactic and Lexical Knowledge Daniel Olsher	17:00 - 17:30	Full Spectrum Opinion Mining: Integrating Domain, Syntactic and Lexical Knowledge	Full Spectrum Opinion Mining: Integrating Domain, Syntactic and Lexical Knowledge	17:00 - 17:30	Full Spectrum Opinion Mining: Integrating Domain, Syntactic and Lexical Knowledge Daniel Olsher
17:30 - 18:00	Concluding Remarks Erik Cambria	17:30 - 18:00	Concluding Remarks Erik Cambria	17:30 - 18:00	Concluding Remarks	Concluding Remarks	17:30 - 18:00	Concluding Remarks Erik Cambria

SMAM: Workshop on Social Media Analysis and Mining

Organized by Qiuming Zhu and Nolan Hemmatazad
08:30 - 10:45
Room: Alto + Mezzo
https://sites.google.com/a/unomaha.edu/smam/

Online social networks like Facebook and Twitter have witnessed explosive growth over the course of the past decade. The use of these networks and the social features of other sites or applications intersects with nearly every facet of our lives, defining and evolving the nature of our social interactions. This phenomenal surge of interest and adoption of online social networks and other social computing utilities creates an outstanding opportunity for those interested in collecting or analyzing real world data. Further, it provides a testing ground for exploring new methods of analyzing and handling data at scales seldom seen before. The goal of the workshop on Social Media Analysis and Mining (SMAM 2012) is to provide a forum for academic researchers, students, practitioners, and others with an interest in online social network analysis and data mining concepts to come together and share their ideas, network, and learn new methods for tackling complex problems in these exciting fields.

08:30 - 08:45	Welcoming and Introduction Qiuming Zhu	08:30 - 08:45	Welcoming and Introduction Qiuming Zhu	08:30 - 08:45	Welcoming and Introduction	Welcoming and Introduction	08:30 - 08:45	Welcoming and Introduction Qiuming Zhu
08:45 - 09:05	Online Social Behavior in Twitter: A Literature Review Olav Aarts, Peter-Paul van Maanen, Tanneke Ouboter and Jan Maarten Schraagen	08:45 - 09:05	Online Social Behavior in Twitter: A Literature Review Olav Aarts, Peter-Paul van Maanen, Tanneke Ouboter and Jan Maarten Schraagen	08:45 - 09:05	Online Social Behavior in Twitter: A Literature Review	Online Social Behavior in Twitter: A Literature Review	08:45 - 09:05	Online Social Behavior in Twitter: A Literature Review Olav Aarts, Peter-Paul van Maanen, Tanneke Ouboter and Jan Maarten Schraagen
09:05 - 09:25	Twitter Volume, Current Spending, and Weekday Norms Predict Consumer Spending Three Days in Advance Justin Stewart, Homer Strong and Mark Bedau	09:05 - 09:25	Twitter Volume, Current Spending, and Weekday Norms Predict Consumer Spending Three Days in Advance Justin Stewart, Homer Strong and Mark Bedau	09:05 - 09:25	Twitter Volume, Current Spending, and Weekday Norms Predict Consumer Spending Three Days in Advance	Twitter Volume, Current Spending, and Weekday Norms Predict Consumer Spending Three Days in Advance	09:05 - 09:25	Twitter Volume, Current Spending, and Weekday Norms Predict Consumer Spending Three Days in Advance Justin Stewart, Homer Strong and Mark Bedau
09:25 - 09:45	Geosocial Graph Based Community Detection Yves van Gennip, Huiyi Hu, Blake Hunter, Mason A. Porter	09:25 - 09:45	Geosocial Graph Based Community Detection Yves van Gennip, Huiyi Hu, Blake Hunter, Mason A. Porter	09:25 - 09:45	Geosocial Graph Based Community Detection	Geosocial Graph Based Community Detection	09:25 - 09:45	Geosocial Graph Based Community Detection Yves van Gennip, Huiyi Hu, Blake Hunter, Mason A. Porter
09:45 - 10:15	Invited Industry Talk Misia Tramp	09:45 - 10:15	Invited Industry Talk Misia Tramp	09:45 - 10:15	Invited Industry Talk	Invited Industry Talk	09:45 - 10:15	Invited Industry Talk Misia Tramp
10:15 - 10:45	Coffee Break	10:15 - 10:45	Coffee Break	10:15 - 10:45	Coffee Break	Coffee Break	10:15 - 10:45	Coffee Break
10:45 - 12:30	WEMA Workshop WEMA	10:45 - 12:30	WEMA Workshop WEMA	10:45 - 12:30	WEMA Workshop	WEMA Workshop	10:45 - 12:30	WEMA Workshop WEMA

SSTDM: Workshop on Spatial and Spatiotemporal Data Mining

Organized by Shashi Shekar, Peggy Agouris, Ranga Raju Vatsavai, Bart Kuijpers, Nanni Mirco, Donato Malerba and Anthony Stefanidis
09:00 - 17:30
Room: Holbein
http://www.ornl.gov/sci/knowledgediscovery/sstdm-12

This workshop features three invited talks and ten peer-reviewed technical presentations covering latest advances in spatial and spatiotemporal data mining. We cordially invite all data miners to this exciting workshop.

09:00 - 09:10	Welcome and Introduction Bart Kuijpers	09:00 - 09:10	Welcome and Introduction Bart Kuijpers	09:00 - 09:10	Welcome and Introduction	Welcome and Introduction	09:00 - 09:10	Welcome and Introduction Bart Kuijpers
09:10 - 10:00	Keynote Talk	09:10 - 10:00	Keynote Talk	09:10 - 10:00	Keynote Talk	Keynote Talk	09:10 - 10:00	Keynote Talk
10:00 - 10:25	Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents Corrado Loglisci, Dino Ienco, Mathieu Roche, Maguelonne Teisseire, and Donato Malerba	10:00 - 10:25	Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents Corrado Loglisci, Dino Ienco, Mathieu Roche, Maguelonne Teisseire, and Donato Malerba	10:00 - 10:25	Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents	Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents	10:00 - 10:25	Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents Corrado Loglisci, Dino Ienco, Mathieu Roche, Maguelonne Teisseire, and Donato Malerba
10:30 - 11:00	Coffee Break	10:30 - 11:00	Coffee Break	10:30 - 11:00	Coffee Break	Coffee Break	10:30 - 11:00	Coffee Break
11:00 - 11:25	Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis Doel Gonzalez, Zhengzhang Chen, Isaac Tetteh, Tatdow Pansombut, Fredrick Semazzi, Vipin Kumar, Anatoli Melechko, and Nagiza Samatova	11:00 - 11:25	Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis Doel Gonzalez, Zhengzhang Chen, Isaac Tetteh, Tatdow Pansombut, Fredrick Semazzi, Vipin Kumar, Anatoli Melechko, and Nagiza Samatova	11:00 - 11:25	Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis	Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis	11:00 - 11:25	Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis Doel Gonzalez, Zhengzhang Chen, Isaac Tetteh, Tatdow Pansombut, Fredrick Semazzi, Vipin Kumar, Anatoli Melechko, and Nagiza Samatova
11:25 - 11:50	Approximate Search on Massive Spatiotemporal Datasets Ivan Brugere, Karsten Steinhaeuser, Shyam Boriah, and Vipin Kumar	11:25 - 11:50	Approximate Search on Massive Spatiotemporal Datasets Ivan Brugere, Karsten Steinhaeuser, Shyam Boriah, and Vipin Kumar	11:25 - 11:50	Approximate Search on Massive Spatiotemporal Datasets	Approximate Search on Massive Spatiotemporal Datasets	11:25 - 11:50	Approximate Search on Massive Spatiotemporal Datasets Ivan Brugere, Karsten Steinhaeuser, Shyam Boriah, and Vipin Kumar
12:00 - 13:30	Lunch Break Break	12:00 - 13:30	Lunch Break Break	12:00 - 13:30	Lunch Break Break	Lunch Break Break	12:00 - 13:30	Lunch Break Break
13:30 - 14:00	Invited Talk	13:30 - 14:00	Invited Talk	13:30 - 14:00	Invited Talk	Invited Talk	13:30 - 14:00	Invited Talk
14:00 - 14:25	Geo-referenced Time-series Summarization Using k-Full Trees: A Summary of Results Dev Oliver, Shashi Shekhar, James Kang, Renee Bousselaire, Veronica Carlan, and Michael Evans	14:00 - 14:25	Geo-referenced Time-series Summarization Using k-Full Trees: A Summary of Results Dev Oliver, Shashi Shekhar, James Kang, Renee Bousselaire, Veronica Carlan, and Michael Evans	14:00 - 14:25	Geo-referenced Time-series Summarization Using k-Full Trees: A Summary of Results	Geo-referenced Time-series Summarization Using k-Full Trees: A Summary of Results	14:00 - 14:25	Geo-referenced Time-series Summarization Using k-Full Trees: A Summary of Results Dev Oliver, Shashi Shekhar, James Kang, Renee Bousselaire, Veronica Carlan, and Michael Evans
14:25 - 14:50	Mining Spatio-temporal Patterns in the Presence of Concept Hierarchies Le Van Quoc Anh and Michael Gertz	14:25 - 14:50	Mining Spatio-temporal Patterns in the Presence of Concept Hierarchies Le Van Quoc Anh and Michael Gertz	14:25 - 14:50	Mining Spatio-temporal Patterns in the Presence of Concept Hierarchies	Mining Spatio-temporal Patterns in the Presence of Concept Hierarchies	14:25 - 14:50	Mining Spatio-temporal Patterns in the Presence of Concept Hierarchies Le Van Quoc Anh and Michael Gertz
14:50 - 15:15	Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions JKarthik Ganesan Pillai, Rafal Angryk, and Juan Banda	14:50 - 15:15	Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions JKarthik Ganesan Pillai, Rafal Angryk, and Juan Banda	14:50 - 15:15	Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions	Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions	14:50 - 15:15	Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions JKarthik Ganesan Pillai, Rafal Angryk, and Juan Banda
15:15 - 15:30	Spatial Interestingness Measures for Co-location Pattern Miningt Christian Sengstock, Michael Gertz, and Canh Tran Van	15:15 - 15:30	Spatial Interestingness Measures for Co-location Pattern Miningt Christian Sengstock, Michael Gertz, and Canh Tran Van	15:15 - 15:30	Spatial Interestingness Measures for Co-location Pattern Miningt	Spatial Interestingness Measures for Co-location Pattern Miningt	15:15 - 15:30	Spatial Interestingness Measures for Co-location Pattern Miningt Christian Sengstock, Michael Gertz, and Canh Tran Van
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 16:30	Invited Talk	16:00 - 16:30	Invited Talk	16:00 - 16:30	Invited Talk	Invited Talk	16:00 - 16:30	Invited Talk
16:30 - 16:45	Performance-Optimizing Classification of Time-series based on Nearest Neighbor Density Approximation Shin Ando	16:30 - 16:45	Performance-Optimizing Classification of Time-series based on Nearest Neighbor Density Approximation Shin Ando	16:30 - 16:45	Performance-Optimizing Classification of Time-series based on Nearest Neighbor Density Approximation	Performance-Optimizing Classification of Time-series based on Nearest Neighbor Density Approximation	16:30 - 16:45	Performance-Optimizing Classification of Time-series based on Nearest Neighbor Density Approximation Shin Ando
16:45 - 17:00	Comparative Study of Association Rule Mining and MiSTIC in Extracting Spatio-Temporal Disease Occurrences Patterns Vipul Raheja and Krishnan Rajan	16:45 - 17:00	Comparative Study of Association Rule Mining and MiSTIC in Extracting Spatio-Temporal Disease Occurrences Patterns Vipul Raheja and Krishnan Rajan	16:45 - 17:00	Comparative Study of Association Rule Mining and MiSTIC in Extracting Spatio-Temporal Disease Occurrences Patterns	Comparative Study of Association Rule Mining and MiSTIC in Extracting Spatio-Temporal Disease Occurrences Patterns	16:45 - 17:00	Comparative Study of Association Rule Mining and MiSTIC in Extracting Spatio-Temporal Disease Occurrences Patterns Vipul Raheja and Krishnan Rajan
17:00 - 17:15	Location Extraction from Social Networks with Commodity Software and Online Data Dimitrios Gunopulos	17:00 - 17:15	Location Extraction from Social Networks with Commodity Software and Online Data Dimitrios Gunopulos	17:00 - 17:15	Location Extraction from Social Networks with Commodity Software and Online Data	Location Extraction from Social Networks with Commodity Software and Online Data	17:00 - 17:15	Location Extraction from Social Networks with Commodity Software and Online Data Dimitrios Gunopulos
17:15 - 17:30	Closing Remarks	17:15 - 17:30	Closing Remarks	17:15 - 17:30	Closing Remarks	Closing Remarks	17:15 - 17:30	Closing Remarks

WEMA: Workshop on Web Entity Modeling and Applications

Organized by Jun Yan, Bin Gao, Dou Shen, and Zheng Chen
10:45 - 12:30
Room: Alto & Mezzo
http://research.microsoft.com/en-us/um/beijing/events/wema-2012

Web entities have been recognized as one of the key things, which can take real semantics to our current keyword based Web. Entity related research such as entity extraction, entity recognition, entity relation modeling, and entity disambiguation is highly desired by recent advances in various Web applications such as Web search, online advertising, and social networks. As some application examples, without entities being correctly recognized and disambiguated in search queries, the users' search intents are hard to be well understood by search engines. The relation model of people, which is a special kind of entity, is the foundation of social networks mining. The intelligent online services such as “best guess” in Google and “Siri” of Apple highly depend on whether the entities and corresponding concepts in user questions are well recognized and classified. Thus, entity modeling has been an attractive inter-disciplinary research field, interplaying with information extraction, Web mining, machine learning, Natural Language Processing, information retrieval, computational advertising, social network, etc. Taking entity as the theme, this workshop is to bring together leading researchers in related areas to promote this important but underexplored research direction, establish the technical foundation, and assess the state of the art.

10:45 - 10:50	Welcoming and Introduction Jun Yan	10:45 - 10:50	Welcoming and Introduction Jun Yan	10:45 - 10:50	Welcoming and Introduction	Welcoming and Introduction	10:45 - 10:50	Welcoming and Introduction Jun Yan
10:50 - 11:30	Keynote Talk: Semantic Data Mining: Leveraging Entity Modeling for Knowledge Discovery Ruoming Jin	10:50 - 11:30	Keynote Talk: Semantic Data Mining: Leveraging Entity Modeling for Knowledge Discovery Ruoming Jin	10:50 - 11:30	Keynote Talk: Semantic Data Mining: Leveraging Entity Modeling for Knowledge Discovery	Keynote Talk: Semantic Data Mining: Leveraging Entity Modeling for Knowledge Discovery	10:50 - 11:30	Keynote Talk: Semantic Data Mining: Leveraging Entity Modeling for Knowledge Discovery Ruoming Jin
11:30 - 11:50	Constructing and Exploring Composite Items Using Max-valid Bundles Gowtham Srinivas, Sreyantha Chary, Satheesh Kumar D, and Santhi Thilagam	11:30 - 11:50	Constructing and Exploring Composite Items Using Max-valid Bundles Gowtham Srinivas, Sreyantha Chary, Satheesh Kumar D, and Santhi Thilagam	11:30 - 11:50	Constructing and Exploring Composite Items Using Max-valid Bundles	Constructing and Exploring Composite Items Using Max-valid Bundles	11:30 - 11:50	Constructing and Exploring Composite Items Using Max-valid Bundles Gowtham Srinivas, Sreyantha Chary, Satheesh Kumar D, and Santhi Thilagam
11:50 - 12:10	Learning to Extract Entity Uniqueness from Web for Helping User Decision Making Wenhan Wang, Ning Liu, and Yiran Xie	11:50 - 12:10	Learning to Extract Entity Uniqueness from Web for Helping User Decision Making Wenhan Wang, Ning Liu, and Yiran Xie	11:50 - 12:10	Learning to Extract Entity Uniqueness from Web for Helping User Decision Making	Learning to Extract Entity Uniqueness from Web for Helping User Decision Making	11:50 - 12:10	Learning to Extract Entity Uniqueness from Web for Helping User Decision Making Wenhan Wang, Ning Liu, and Yiran Xie
12:10 - 12:30	Endless and Scalable Knowledge Table Extraction from Semi-structured Websites Yingqin Gu, Lei JI, Ziheng Jiang, and Jun He	12:10 - 12:30	Endless and Scalable Knowledge Table Extraction from Semi-structured Websites Yingqin Gu, Lei JI, Ziheng Jiang, and Jun He	12:10 - 12:30	Endless and Scalable Knowledge Table Extraction from Semi-structured Websites	Endless and Scalable Knowledge Table Extraction from Semi-structured Websites	12:10 - 12:30	Endless and Scalable Knowledge Table Extraction from Semi-structured Websites Yingqin Gu, Lei JI, Ziheng Jiang, and Jun He

Industry and Government Track

Organized by David Martens and Bart Goethals
09:00-17:30
Room: Salle des Nation II
http://icdm2012.ua.ac.be/industry-government

The Industry and Government Track of the IEEE ICDM conference will bring together academics and practitioners to discuss data mining challenges and opportunities that are emerging in both industry and government. This year we see a strong interest on Big Data. Seen that data mining has been considered as one of the top technological trends for companies, we hope that the track will further demonstrate the potential of data mining for both industry and government, and spur collaborations between academics and practitioners in this area.

09:00 - 09:30	Keynote Talk: Human vs. Machine: How Watson beat the all-time best Jeopardy champions James Fan (IBM)	09:00 - 09:30	Keynote Talk: Human vs. Machine: How Watson beat the all-time best Jeopardy champions James Fan (IBM)	09:00 - 09:30	Keynote Talk: Human vs. Machine: How Watson beat the all-time best Jeopardy champions	Keynote Talk: Human vs. Machine: How Watson beat the all-time best Jeopardy champions	09:00 - 09:30	Keynote Talk: Human vs. Machine: How Watson beat the all-time best Jeopardy champions James Fan (IBM)
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break	10:00 - 10:30	Coffee Break
10:30 - 11:00	Data mining lessons from half a century of credit scoring Tony Van Gestel (Dexia)	10:30 - 11:00	Data mining lessons from half a century of credit scoring Tony Van Gestel (Dexia)	10:30 - 11:00	Data mining lessons from half a century of credit scoring	Data mining lessons from half a century of credit scoring	10:30 - 11:00	Data mining lessons from half a century of credit scoring Tony Van Gestel (Dexia)
11:00 - 11:30	Tectonic Shifts in Television Advertising Brendan Kitts (PrecisionDemand)	11:00 - 11:30	Tectonic Shifts in Television Advertising Brendan Kitts (PrecisionDemand)	11:00 - 11:30	Tectonic Shifts in Television Advertising	Tectonic Shifts in Television Advertising	11:00 - 11:30	Tectonic Shifts in Television Advertising Brendan Kitts (PrecisionDemand)
11:30 - 12:00	Data mining for official statistics Bart Buelens (Statistics Netherlands)	11:30 - 12:00	Data mining for official statistics Bart Buelens (Statistics Netherlands)	11:30 - 12:00	Data mining for official statistics	Data mining for official statistics	11:30 - 12:00	Data mining for official statistics Bart Buelens (Statistics Netherlands)
12:00 - 13:30	Lunch Break	12:00 - 13:30	Lunch Break	12:00 - 13:30	Lunch Break	Lunch Break	12:00 - 13:30	Lunch Break
13:30 - 14:30	Keynote Talk: Mining (Massive) Consumer Behavior Data for Marketing Foster Provost (New York University)	13:30 - 14:30	Keynote Talk: Mining (Massive) Consumer Behavior Data for Marketing Foster Provost (New York University)	13:30 - 14:30	Keynote Talk: Mining (Massive) Consumer Behavior Data for Marketing	Keynote Talk: Mining (Massive) Consumer Behavior Data for Marketing	13:30 - 14:30	Keynote Talk: Mining (Massive) Consumer Behavior Data for Marketing Foster Provost (New York University)
14:30 - 15:00	Data Mining Framework For Monitoring Nuclear Facilities Ranga Raju Vatsavai (Oak Ridge National Laboratory, US)	14:30 - 15:00	Data Mining Framework For Monitoring Nuclear Facilities Ranga Raju Vatsavai (Oak Ridge National Laboratory, US)	14:30 - 15:00	Data Mining Framework For Monitoring Nuclear Facilities	Data Mining Framework For Monitoring Nuclear Facilities	14:30 - 15:00	Data Mining Framework For Monitoring Nuclear Facilities Ranga Raju Vatsavai (Oak Ridge National Laboratory, US)
15:00 - 15:30	Distributed Big Advertiser Data Mining Ashish Bindra (nPario)	15:00 - 15:30	Distributed Big Advertiser Data Mining Ashish Bindra (nPario)	15:00 - 15:30	Distributed Big Advertiser Data Mining	Distributed Big Advertiser Data Mining	15:00 - 15:30	Distributed Big Advertiser Data Mining Ashish Bindra (nPario)
15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	15:30 - 16:00	Coffee Break	Coffee Break	15:30 - 16:00	Coffee Break
16:00 - 17:00	Keynote Talk: Increased efficiency of fraud inspection through Data Mining John Crombez (Belgian State Secretary for the Fight against Social and Tax Fraud)	16:00 - 17:00	Keynote Talk: Increased efficiency of fraud inspection through Data Mining John Crombez (Belgian State Secretary for the Fight against Social and Tax Fraud)	16:00 - 17:00	Keynote Talk: Increased efficiency of fraud inspection through Data Mining	Keynote Talk: Increased efficiency of fraud inspection through Data Mining	16:00 - 17:00	Keynote Talk: Increased efficiency of fraud inspection through Data Mining John Crombez (Belgian State Secretary for the Fight against Social and Tax Fraud)
17:00 - 17:30	Automation of prediction of rare events in big data: is it possible (today)? Thierry Van de Merckt (BISide, Solvay Business School)	17:00 - 17:30	Automation of prediction of rare events in big data: is it possible (today)? Thierry Van de Merckt (BISide, Solvay Business School)	17:00 - 17:30	Automation of prediction of rare events in big data: is it possible (today)?	Automation of prediction of rare events in big data: is it possible (today)?	17:00 - 17:30	Automation of prediction of rare events in big data: is it possible (today)? Thierry Van de Merckt (BISide, Solvay Business School)
17:30 – 18:00	Big Data and Fraud detection in government and banking, Lessons learned so far. Jerome Bryssinck (SAS Institute)	17:30 – 18:00	Big Data and Fraud detection in government and banking, Lessons learned so far. Jerome Bryssinck (SAS Institute)	17:30 – 18:00	Big Data and Fraud detection in government and banking, Lessons learned so far.	Big Data and Fraud detection in government and banking, Lessons learned so far.	17:30 – 18:00	Big Data and Fraud detection in government and banking, Lessons learned so far. Jerome Bryssinck (SAS Institute)

CoClus: Workshop on Coclustering and Applications

Organized by Stéphane Canu, Gérard Govaert and Mohamed Nadif
14:00-18:00
Room: Tintoretto I
https://sites.google.com/site/workshopcoclustering

Co-clustering is an important tool in a variety of scientific areas including document clustering, bioinformatics and information retrieval. Compared with the classical clustering algorithms, co-clustering algorithms have been shown to be more effective in discovering certain hidden clustering structures in data. This workshop intends to provide a forum for researchers in the field of Machine Learning, Statistics, Bioinformatics and Data Mining to discuss the above and other related topics regarding co-clustering and their applications.

14:00 – 14:15	Welcoming and Introduction Mohamed Nadif	14:00 – 14:15	Welcoming and Introduction Mohamed Nadif	14:00 – 14:15	Welcoming and Introduction	Welcoming and Introduction	14:00 – 14:15	Welcoming and Introduction Mohamed Nadif
14:15-14:45	Biclustering of high-throughput gene expression data with BiclusterMiner Asta Laiho, Andras Kiraly, Janos Abonyi and Attila Gyenesei	14:15-14:45	Biclustering of high-throughput gene expression data with BiclusterMiner Asta Laiho, Andras Kiraly, Janos Abonyi and Attila Gyenesei	14:15-14:45	Biclustering of high-throughput gene expression data with BiclusterMiner	Biclustering of high-throughput gene expression data with BiclusterMiner	14:15-14:45	Biclustering of high-throughput gene expression data with BiclusterMiner Asta Laiho, Andras Kiraly, Janos Abonyi and Attila Gyenesei
14:45-15:15	Mining Local Staircase Patterns in Noisy Data Thanh Le Van, Carolina Fierro, Tias Guns, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt and Kathleen Marchal	14:45-15:15	Mining Local Staircase Patterns in Noisy Data Thanh Le Van, Carolina Fierro, Tias Guns, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt and Kathleen Marchal	14:45-15:15	Mining Local Staircase Patterns in Noisy Data	Mining Local Staircase Patterns in Noisy Data	14:45-15:15	Mining Local Staircase Patterns in Noisy Data Thanh Le Van, Carolina Fierro, Tias Guns, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt and Kathleen Marchal
15:30-16:00	Coffee Break	15:30-16:00	Coffee Break	15:30-16:00	Coffee Break	Coffee Break	15:30-16:00	Coffee Break
16:00-16:30	TeamFinder: A Co-clustering based Framework for Finding an Effective Team of Experts in Social Networks Farnoush Farhadi, Elham Hoseini, Sattar Hashemi and Ali Hamzeh	16:00-16:30	TeamFinder: A Co-clustering based Framework for Finding an Effective Team of Experts in Social Networks Farnoush Farhadi, Elham Hoseini, Sattar Hashemi and Ali Hamzeh	16:00-16:30	TeamFinder: A Co-clustering based Framework for Finding an Effective Team of Experts in Social Networks	TeamFinder: A Co-clustering based Framework for Finding an Effective Team of Experts in Social Networks	16:00-16:30	TeamFinder: A Co-clustering based Framework for Finding an Effective Team of Experts in Social Networks Farnoush Farhadi, Elham Hoseini, Sattar Hashemi and Ali Hamzeh
16:30-17:00	Concept-based Biclustering for Internet Advertisement Dmitry Ignatov, Sergei Kuznetsov and Jonas Poelmans	16:30-17:00	Concept-based Biclustering for Internet Advertisement Dmitry Ignatov, Sergei Kuznetsov and Jonas Poelmans	16:30-17:00	Concept-based Biclustering for Internet Advertisement	Concept-based Biclustering for Internet Advertisement	16:30-17:00	Concept-based Biclustering for Internet Advertisement Dmitry Ignatov, Sergei Kuznetsov and Jonas Poelmans
17:00-17:30	An approximation of the integrated classification likelihood for the latent block model Aurore Lomet, Gérard Govaert, and Yves Grandvalet	17:00-17:30	An approximation of the integrated classification likelihood for the latent block model Aurore Lomet, Gérard Govaert, and Yves Grandvalet	17:00-17:30	An approximation of the integrated classification likelihood for the latent block model	An approximation of the integrated classification likelihood for the latent block model	17:00-17:30	An approximation of the integrated classification likelihood for the latent block model Aurore Lomet, Gérard Govaert, and Yves Grandvalet
17:30-18:00	A Triclustering Approach for Time Evolving Graphs Romain Guigours, Marc Boullé and Fabrice Rossi	17:30-18:00	A Triclustering Approach for Time Evolving Graphs Romain Guigours, Marc Boullé and Fabrice Rossi	17:30-18:00	A Triclustering Approach for Time Evolving Graphs	A Triclustering Approach for Time Evolving Graphs	17:30-18:00	A Triclustering Approach for Time Evolving Graphs Romain Guigours, Marc Boullé and Fabrice Rossi

Information

Program

Attending

You are here

Workshop Details

BioDM: Workshop on Biological Data Mining and its Applications in Healthcare

COSTS: Workshop on Cost Sensitive Data Mining

CPROD1: Workshop on the ICDM 2012 Contest on Consumer PRODuct Mention Recognition and Linking

DaMNet: Workshop on Data Mining in Networks

DMS: Workshop on Data Mining for Service

DPADM: IEEE ICDM 2012 International Workshop on Discrimination and Privacy-Aware Data Mining

KDCloud: Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms

OEDM: Workshop on Optimization Based Techniques for Emerging Data Mining

PhD Forum - ICDM 2012

PinSoDa: Workshop on Privacy in Social Data

PTDM: Workshop on Practical Theories of Data Mining

RIKD: Workshop on Reliability Issues in Knowledge Discovery

SENTIRE: Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction

SMAM: Workshop on Social Media Analysis and Mining

SSTDM: Workshop on Spatial and Spatiotemporal Data Mining

WEMA: Workshop on Web Entity Modeling and Applications

Industry and Government Track

CoClus: Workshop on Coclustering and Applications

Search form

Information

Program

Attending

You are here

Workshop Details