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Data Mining and Knowledge Discovery Handbook pp 641–654Cite as

Data Mining Model Comparison

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Summary

The aim of this contribution is to illustrate the role of statistical models and, more generally, of statistics, in choosing a Data Mining model. After a preliminary introduction on the distinction between Data Mining and statistics, we will focus on the issue of how to choose a Data Mining methodology. This well illustrates how statistical thinking can bring real added value to a Data Mining analysis, as otherwise it becomes rather difficult to make a reasoned choice. In the third part of the paper we will present, by means of a case study in credit risk management, how Data Mining and statistics can profitably interact.

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Author information

Authors and Affiliations

  1. Faculty of Economics, University of Pavia, Pavia, Italy

    Paolo Giudici

Authors
  1. Paolo Giudici
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Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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© 2009 Springer Science+Business Media, LLC

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Giudici, P. (2009). Data Mining Model Comparison. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_32

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_32

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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