DIMACS Center, CoRE Building, Rutgers University

**Organizers:****James Abello**, DIMACS, abello@dimacs.rutgers.edu**Graham Cormode**, Bell Laboratories, cormode@bell-labs.com

Data Mining is now a staple part of Computer Science, and has been applied in a wide variety of different areas. It covers a diverse set of topics from algorithms, statistics and discrete mathematics, with the general goal of identifying patterns in data in order to draw inferences and make predictions. This tutorial brings together experts from Data Mining to introduce the key ideas and techniques from:

- Probability, Decision Trees and Bayesian Statistics http://www.cs.cmu.edu/~awm/
- Machine Learning, Classifiers and Boosting http://www.cs.princeton.edu/~schapire/
- Data Stream Analysis and Clustering http://dimacs.rutgers.edu/~graham/pubs/epidcluster.pdf
- Graph Mining http://www.mgvis.com
- Applications to Biology and Epidemiology

The goal is to allow people with little or no knowledge of data mining to understand the basic techniques, and get a flavor of the general methodology and style of results. This tutorial is aimed to be of interest to researchers wishing to work in data mining, and also to researchers from outside computer science who wish to understand these methods in order to apply them. The tutorial includes short talks on applications to problems in epidemiology and biology in order to put the general techniques described into perspective.

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Document last modified on February 21, 2006.