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Supervised Pattern Mining and Applications to Classification

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Frequent Pattern Mining

Abstract

In this chapter we describe the use of patterns in the analysis of supervised data. We survey the different settings for finding patterns as well as sets of patterns. The pattern mining settings are categorized according to whether they include class labels as attributes in the data or whether they partition the data based on these labels. The pattern set mining settings are categorized along several dimensions, including whether they perform iterative mining or post-processing, operate globally or locally, and whether they use patterns directly or indirectly for prediction.

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Author information

Authors and Affiliations

  1. INSA Lyon, LIRIS CNRS UMR 5205, Bâtiment Blaise Pascal, 69621, Villeurbanne, CEDEX, France

    Albrecht Zimmermann

  2. KU Leuven, Celestijnenlaan 200 A, 3001, Leuven, Belgium

    Siegfried Nijssen

  3. Universiteit Leiden, Niels Bohrweg 1, 2333, Leiden, CA, The Netherlands

    Siegfried Nijssen

Authors
  1. Albrecht Zimmermann
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  2. Siegfried Nijssen
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Correspondence to Albrecht Zimmermann .

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Editors and Affiliations

  1. IBM, Yorktown Heights, New York, USA

    Charu C. Aggarwal

  2. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Jiawei Han

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Zimmermann, A., Nijssen, S. (2014). Supervised Pattern Mining and Applications to Classification. In: Aggarwal, C., Han, J. (eds) Frequent Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-07821-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07820-5

  • Online ISBN: 978-3-319-07821-2

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