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International Conference on Formal Concept Analysis

ICFCA 2010: Formal Concept Analysis pp 51–60Cite as

Statistical Methods for Data Mining and Knowledge Discovery

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5986))

Abstract

This survey paper aims mainly at giving computer scientists a rapid bird’s eye view, from a mathematician’s perspective, of the main statistical methods used in order to extract knowledge from databases comprising various types of observations. After touching briefly upon the matters of supervision, data regularization and a brief review of the main models, the key issues of model assessment, selection and inference are perused. Finally, specific statistical problems arising from applications around data mining and warehousing are explored. Examples and applications are chosen mainly from the vast collection of image and video retrieval, indexation and classification challenges facing us today.

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Author information

Authors and Affiliations

  1. UQO, Gatineau, QC, J8X 3X7, Canada

    Jean Vaillancourt

Authors
  1. Jean Vaillancourt
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Editor information

Editors and Affiliations

  1. School of Engineering, Zurich University of Applied Sciences, Technikumstraße 9, 8401, Winterthur, Switzerland

    Léonard Kwuida

  2. SAP Research Center, Chemnitzer Straße 48, 01187, Dresden, Germany

    Barış Sertkaya

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Vaillancourt, J. (2010). Statistical Methods for Data Mining and Knowledge Discovery. In: Kwuida, L., Sertkaya, B. (eds) Formal Concept Analysis. ICFCA 2010. Lecture Notes in Computer Science(), vol 5986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11928-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-11928-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11927-9

  • Online ISBN: 978-3-642-11928-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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