On Mining Scientific Datasets

Data mining techniques have gained acceptance as a viable means of finding useful information in data. While the techniques can be applied to any kind of data, a brief survey of the work presented at recent conferences in data mining and knowledge discovery might lead one to believe that these techniques are being applied mainly to commercial data sets, to address problems such as customer relationship management, market basket analysis, credit card fraud, etc. Often overlooked is the fact that data mining techniques have long been applied to scientific datasets, with fields such as remote sensing, astronomy, biology, physics, and chemistry, 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 important role and discuss some of the issues that make scientific data mining different from its commercial counterpart. I show that the diversity of applications, the richness of the problems faced by practitioners, and the opportunity to borrow ideas from other domains, make scientific data mining an exciting and challenging field.