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Data Mining and Constraint Programming pp 25–48Cite as

Data Mining and Constraints: An Overview

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10101))

Abstract

This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints requires mechanisms for defining and evaluating them during the knowledge extraction process. We give a structured account of three main groups of constraints based on the specific context in which they are defined and used. The aim is to provide a complete view on constraints as a building block of data mining methods.

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  1. Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy

    Valerio Grossi, Dino Pedreschi & Franco Turini

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  1. Valerio Grossi
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  2. Dino Pedreschi
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  3. Franco Turini
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Correspondence to Valerio Grossi .

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Editors and Affiliations

  1. Université Montpellier 2, Montpellier, France

    Christian Bessiere

  2. KU Leuven, Heverlee, Belgium

    Luc De Raedt

  3. University of British Columbia, Vancouver, British Columbia, Canada

    Lars Kotthoff

  4. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Siegfried Nijssen

  5. University College Cork, Cork, Ireland

    Barry O'Sullivan

  6. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Grossi, V., Pedreschi, D., Turini, F. (2016). Data Mining and Constraints: An Overview. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_2

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