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Overview of Overlapping Partitional Clustering Methods

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Partitional Clustering Algorithms

Abstract

Identifying non-disjoint clusters is an important issue in clustering referred to as Overlapping Clustering. While traditional clustering methods ignore the possibility that an observation can be assigned to several groups and lead to k exhaustive and exclusive clusters representing the data, Overlapping Clustering methods offer a richer model for fitting existing structures in several applications requiring a non-disjoint partitioning. In fact, the issue of overlapping clustering has been studied since the last four decades leading to several methods in the literature adopting many usual approaches such as hierarchical, generative, graphical and k-means based approach. We review in this paper the fundamental concepts of overlapping clustering while we survey the widely known overlapping partitional clustering algorithms and the existing techniques to evaluate the quality of non-disjoint partitioning. Furthermore, a comparative theoretical and experimental study of used techniques to model overlaps is given over different multi-labeled benchmarks.

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Notes

  1. 1.

    The original objective function of PCM takes into account the identification of outliers, whereas we give in this paper a short structure of the objective function to facilitate the comparison of PCM with the other described methods.

  2. 2.

    The original objective function of ECM takes into account the identification of outliers by considering \(\pi _{i\varnothing }\) a mass of belief to belong to any cluster, whereas we consider in this paper that all combinations of clusters are tolerated except the empty set (\(A_{j}\neq \varnothing \)) in order to facilitate the comparison of ECM with the other described methods.

  3. 3.

    cf. http://www.grouplens.org/node/76.

  4. 4.

    cf.http://mlkd.csd.auth.gr/multilabel.html

  5. 5.

    cf.http://mlkd.csd.auth.gr/multilabel.html

  6. 6.

    cf.http://mlkd.csd.auth.gr/multilabel.html

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Authors and Affiliations

  1. LARODEC, ISG Tunis, University of Tunis, 41 Avenue de la Liberté, Cité Bouchoucha, Le Bardo, 2000, Tunisia

    Chiheb-Eddine Ben N’Cir & Nadia Essoussi

  2. LIFO, Université d’Orléans, EA 4022, Orléans, France

    Guillaume Cleuziou

Authors
  1. Chiheb-Eddine Ben N’Cir
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  2. Guillaume Cleuziou
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  3. Nadia Essoussi
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Corresponding author

Correspondence to Guillaume Cleuziou .

Editor information

Editors and Affiliations

  1. Computer Science dept., Louisiana State University Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

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N’Cir, CE.B., Cleuziou, G., Essoussi, N. (2015). Overview of Overlapping Partitional Clustering Methods. In: Celebi, M. (eds) Partitional Clustering Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-09259-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-09259-1_8

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