Statistical Inference and Data Mining (1996)

by Clark Glymour. David Madigan. Daryl Pregibon. Padhraic Smyth.

COMMUNICATIONS OF THE ACM November 1996

DATA MINING AIMS TO DISCOVER SOMETHING NEW FROM THE FACTS RECORDED in a database. For many reasons—encoding errors, measurement errors, unrecorded causes of recorded features—the information in a database is almost always noisy; therefore, inference from databases invites applications of the theory of probability. From a statistical point of view, databases are usually uncontrolled convenience samples; therefore data mining poses a collection of interesting, difficult—sometimes impossible—inference problems, raising many issues, some well studied and others unexplored or at least unsettled. Data mining almost always involves a search architecture requiring evaluation of hypotheses at the stages of the search, evaluation of the search output, and appropriate use of the results. Statistics has little to offer in understanding search architectures but a great deal to offer in evaluation of hypotheses in the course of a search, in evaluating the results of a search, and in understanding the appropriate uses of the results. [via]

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