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Constrained Clustering: Current and New Trends

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Abstract

Clustering is an unsupervised process which aims to discover regularities and underlying structures in data. Constrained clustering extends clustering in such a way that expert knowledge can be integrated through the use of user constraints. These guide the clustering process towards a more relevant result. Different means of integrating constraints into the clustering process exist. They consist of extending classical clustering algorithms, such as the well-known k-means algorithm; modelling the constrained clustering problem using a declarative framework; and finally, by directly integrating constraints into a collaborative process that involves several clustering algorithms. A common point of these approaches is that they require the user constraints to be given before the process begins. New trends in constrained clustering highlight the need for better interaction between the automatic process and expert supervision. This chapter is dedicated to constrained clustering. In particular, after a brief overview of constrained clustering and associated issues, it presents the three main approaches in the domain. It also discusses exploratory data mining by presenting models that develop interaction with the user in an incremental and collaborative way. Finally, moving beyond constraints, some aspects of user implicit preferences and their capture are introduced.

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Authors and Affiliations

  1. ICube, University of Strasbourg, Strasbourg, France

    Pierre Gançarski & Thomas Lampert

  2. LIFO, University of Orléans, Orléans, France

    Thi-Bich-Hanh Dao

  3. Normandy University, UNICAEN, ENSICAEN, CNRS - UMR GREYC, Caen, France

    Bruno Crémilleux

  4. IRIMAS, University of Haute-Alsace, Mulhouse, France

    Germain Forestier

Authors
  1. Pierre Gançarski
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  2. Thi-Bich-Hanh Dao
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  3. Bruno Crémilleux
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  4. Germain Forestier
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  5. Thomas Lampert
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Corresponding author

Correspondence to Pierre Gançarski .

Editor information

Editors and Affiliations

  1. CRIL-CNRS, Université d’Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

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Gançarski, P., Dao, TBH., Crémilleux, B., Forestier, G., Lampert, T. (2020). Constrained Clustering: Current and New Trends. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06167-8_14

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