Statistical Themes and Lessons for Data Mining

  • Authors:
  • Clark Glymour;David Madigan;Daryl Pregibon;Padhraic Smyth

  • Affiliations:
  • Department of Cognitive Psychology, Carnegie Mellon University, Pittsburgh, PA 15213/ E-mail: cg09@andrew.cmu.edu;Department of Statistics, Box 354322, University of Washington, Seattle, WA 98195/ E-mail: madigan@stat.washington.edu;Statistics Research, AT&/T Laboratories, Murray Hill, NJ 07974/ E-mail: daryl@research.att.com;Information and Computer Science, University of California, Irvine, CA 92717/ E-mail: smyth@ics.uci.edu

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 1997

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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.