Tuesday, May 11

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 NewDivide 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 NewLinear Programs for Accuracy Control in Classification and Regression
Bernhard Schölkopf and UpdatedAlex Smola, Australian National University, Canberra and GMD First, Berlin, Germany
12:15-12:40 Combining Supervised and Unsupervised Learning Using Transduction
Kristin Bennett, Organizer

OP99 Home


Program Updates

Speaker Index




MMD, 5/3/99