Data Mining is becoming increasingly important in industry where one would like to make decisions such as to mail/not mail a catalog, how to maximize customer's satisfaction, what message to send on the networks to specific groups of callers, etc. The modeling issues combine methods of pattern recognition, computer science and statistics.
Given database, one would like to design partitions that give accurate description; feature analysis is required to determine where are the information bearing variables; non-parametric techniques and neural networks may possibly be used to achieve very high insight. The goal of data mining is to achieve predictive modeling, based on accuracy and insight.
The period of concentration brought together researchers from industry and university in order to (i) identify the current and future problem areas, (ii) review the mathematical and statistical approaches presently being used, and (iii) discuss and determine which research directions would be most promising.
Click on the titles to find abstracts and/or links to presentation materials
|SCHEDULE for MONDAY, NOVEMBER 18|
|A.Friedman, R.Gulliver, G. Cybenko||Welcome and Orientation|
IBM Watson Research Labs
|Data Mining and its Industrial Applications|
AT & T Research
Indian Institute of Technology
|Computer Science Colloquium: Bandwidth-efficient parallel computation|
|SCHEDULE for TUESDAY, NOVEMBER 19|
Rensselaer Polytechnical Inst.
|Neural networks for data mining and knowledge discovery|
IBM Santa Teresa Labs
University of Minnesota
|Parallel Data Mining Algorithms|
|SCHEDULE for WEDNESDAY, NOVEMBER 20|
Johns Hopkins University
|Towards High-Performance Intelligent Systems for Data Modelling|
GTE Labs, Waltham
|Developing Industrial Data Mining and Knowledge Discovery Applications: an Overview of Issues|
General Motors R & D Center
|Architectures for Data Mining Over Enterprise Intranets|