Tutorials

All of the Tutorials will be held in the combined room Alto & Mezzo & Tempo on the first floor.

The following tutorials have been accepted for presentation at IEEE ICDM 2012:

• Tutorial 1: Outlier Detection in High Dimensional Data.
  Arthur Zimek (University of Alberta), Erich Schubert and Hans-Peter Kriegel (Ludwig-Maximilians Universitat Munchen)
  Tutorial website
  

  Abstract:
  High dimensional data in Euclidean space pose special challenges to data mining
  algorithms. These challenges are often indiscriminately subsumed under the term
  "curse of dimensionality", more concrete aspects being the so-called "distance
  concentration effect", the presence of irrelevant attributes concealing
  relevant information, or simply efficiency issues. In about just the last few
  years, the task of unsupervised outlier detection has found new specialized
  solutions for tackling high dimensional data in Euclidean space. These
  approaches fall under mainly two categories, namely considering or not
  considering subspaces (subsets of attributes) for the definition of outliers.
  The former are specifically addressing the presence of irrelevant attributes,
  the latter do consider the presence of irrelevant attributes implicitly at best
  but are more concerned with general issues of efficiency and effectiveness.
  Nevertheless, both types of specialized outlier detection algorithms tackle
  challenges specific to high dimensional data. In this tutorial, we discuss
  those aspects of the "curse of dimensionality" that are most important for
  outlier detection in detail and survey specialized algorithms for outlier
  detection from both categories.

• Tutorial 2: What is Strange in Large Networks? Graph-based Irregularity and Fraud Detection.
  Leman Akoglu and Christos Faloutsos (Carnegie Mellon University)
  Tutorial website

  Abstract:
  Detecting anomalies and events in data is a vital task, with numerous
  applications in security, finance, health care, law enforcement, and many
  others. While many techniques have been developed in past years for spotting
  outliers and anomalies in unstructured collections of multi-dimensional points,
  with graph data becoming ubiquitous, techniques for structured graph data have
  been of focus recently. As objects in graphs have long-range correlations, a
  set of novel technology has been developed for abnormality detection in graph
  data. The goal of this tutorial is to provide a general, comprehensive
  overview of the state-of-the-art methods for anomaly, event, and fraud
  detection in data represented as graphs. As a key contribution, we provide a
  thorough exploration of both data mining and machine learning algorithms for
  these detection tasks. We give a general framework for the algorithms,
  categorized under various settings: unsupervised vs. (semi-)supervised, for
  static vs. dynamic data. We focus on the scalability and effectiveness aspects of
  the methods, and highlight results on crucial real-world applications,
  including accounting fraud and opinion spam detection.

• Tutorial 3: Mining Uncertain and Probabilistic Data for Big Data Analytics.
  Jian Pei (Simon Fraser University)
  Tutorial website

  Abstract:
  Uncertain data is inherent in many important applications, particularly in the
  context of big data analytics, such as environmental surveillance, healthcare
  informatics, customer-relationship management, market analysis, and
  quantitative economics research. It is almost impossible to avoid modeling and
  analyzing uncertainty and probability in conquering big data. Analyzing and
  mining large collections of uncertain data have become an important task and
  attracted more and more interest from the data mining and industry application
  communities.

  In this tutorial, carrying big data analytics as the grand background, we will
  present a systematic yet compact review on mining uncertain and probabilistic
  data, including motivations and application examples, problems, challenges,
  fundamental principles, state-of-the-art methods, the interesting open problems
  and future directions. We will emphasize big data analytics applications,
  connections among various mining and analytics tasks, fundamental principles,
  and open problems.

  We assume that the audience has the basic concepts of probability and
  statistics. However, no deep background knowledge about statistics, sampling,
  probability, or any other mathematical principles is assumed. We will use
  sufficient examples to explain the ideas and the intuitions.