A statistical perspective on data mining

Abstract

Data mining can be regarded as a collection of methods for drawing inferences from data. The aims of data mining, and some of its methods, overlap with those of classical statistics. However, there are some philosophical and methodological differences. We examine these differences, and we describe three approaches to machine learning that have developed largely independently: classical statistics, Vapnik's statistical learning theory, and computational learning theory. Comparing these approaches, we conclude that statisticians and data miners can profit by studying each other's methods and using a judiciously chosen combination of them.