Tuesday, May 11

MS17
Optimization Challenges in Data Mining

10:45 AM-12:45 PM
Room: Georgia 8

This minisymposium examines optimization problems found in data mining. Large quantities of data have been captured by computerized applications. Robust methods for prediction, clustering and visualization/browsing are needed to allow users to extract useful information from this data. Many of these problems can be framed as optimization problems whose difficulty is compounded when applied to massive high-dimensional databases. The speakers will present recent research on optimization based approaches to the modeling and solution of data mining problems.

Organizer: Kristin P. Bennett
Rensselaer Polytechnic Institute

10:45-11:10 Scaling Clustering Algorithms to Large Databases

Paul Bradley, Usama Fayyad, and Cory Reina, Microsoft Research

11:15-11:40 Divide and Conquer: An Algorithm for Minimizing the Training Sample Misclassification Cost in Two-Group Classification

Antonie Stam, University of Georgia; and A. Pedro Duarte Silva, Universidade Catolica Portuguesa, Porto, Portugal

11:45-12:10 Linear Programs for Accuracy Control in Classification and Regression

Bernhard Schölkopf and Alex Smola, Australian National University, Canberra and GMD First, Berlin, Germany

12:15-12:40 Combining Supervised and Unsupervised Learning Using Transduction

Kristin Bennett, Organizer

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MMD, 5/3/99