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Statistical Themes and Lessons for Data Mining
Authors:Clark Glymour  David Madigan  Daryl Pregibon  Padhraic Smyth
Affiliation:(1) Department of Cognitive Psychology, Carnegie Mellon University, Pittsburgh, PA, 15213;(2) Department of Statistics, University of Washington, Box 354322, Seattle, WA, 98195;(3) AT&T Laboratories, Statistics Research, Murray Hill, NJ, 07974;(4) Information and Computer Science, University of California, Irvine, CA, 92717
Abstract:Data mining is on the interface of Computer Science andStatistics, utilizing advances in both disciplines to make progressin extracting information from large databases. It is an emergingfield that has attracted much attention in a very short period oftime. This article highlights some statistical themes and lessonsthat are directly relevant to data mining and attempts to identifyopportunities where close cooperation between the statistical andcomputational communities might reasonably provide synergy forfurther progress in data analysis.
Keywords:statistics  uncertainty  modeling  bias  variance
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