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Statistical inference and data mining: false discoveries control

In: Compstat 2006 - Proceedings in Computational Statistics

Author

Listed:
  • Stéphane Lallich

    (Université Lyon 2, Equipe de Recherche en Ingénierie des Connaissances)

  • Olivier Teytaud

    (LRI, CNRS-Université Paris-Sud, TAO-Inria)

  • Elie Prudhomme

    (Université Lyon 2, Equipe de Recherche en Ingénierie des Connaissances)

Abstract

Data Mining is characterized by its ability at processing large amounts of data. Among those are the data “features”- variables or association rules that can be derived from them. Selecting the most interesting features is a classical data mining problem. That selection requires a large number of tests from which arise a number of false discoveries. An original non parametric control method is proposed in this paper. A new criterion, UAFWER, defined as the risk of exceeding a pre-set number of false discoveries, is controlled by BS FD, a bootstrap based algorithm that can be used on one- or two-sided problems. The usefulness of the procedure is illustrated by the selection of differentially interesting association rules on genetic data.

Suggested Citation

  • Stéphane Lallich & Olivier Teytaud & Elie Prudhomme, 2006. "Statistical inference and data mining: false discoveries control," Springer Books, in: Alfredo Rizzi & Maurizio Vichi (ed.), Compstat 2006 - Proceedings in Computational Statistics, pages 325-336, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-1709-6_25
    DOI: 10.1007/978-3-7908-1709-6_25
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