On Mining Scientific Datasets

Abstract

Data mining techniques have gained acceptance as a viable meansof finding useful informatin in data. While the techniques can be applied to any kind of data, a brief survey of the work presented at recent conferencesin datamining and knowledge discovery meight lead one to beleive that these techniques are being applied mainly to commercial data ses, to address problems such as customer relationship management, market basket analysis, credit card fraud, etc. Often overlooked is the fact that data miining techniques have long been applied to scientific datasets, with fields such as remote sensing, astronomy, biology, physics, and chemestry, providing a rich environment for the practice of these techniques. In this paper, I describe the various scientific and engineering areas in which data mining is playing an importat role and discuss some of the issues that make scientific datat mining different from its commercial counterpart. I show that the diversity of applications, the richness of the problems faced by practitioners, and the opportuinty to borrow ideas from other domains, make scientific data mining an exciting and challenging field.