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Clustering Algorithms

  • Chapter
Unsupervised Classification

Abstract

This chapter consists of detailed discussions regarding the clustering problem. Different well-known partitional clustering techniques like K-means, K-medoid, and fuzzy C-means are described. This is followed by a discussion on some distribution-based clustering techniques, namely expectation maximization. Hierarchical clustering techniques, like single linkage, average linkage and complete linkage, and density-based clustering techniques, like DB-Scan and GD-Scan, are then described in detail. Some grid-based clustering techniques, e.g., STRING are discussed next. The problem of clustering is thereafter formulated as one of optimization and some evolutionary clustering techniques are described. Finally it is shown how clustering can be posed as a multiobjective optimization problem and some recently developed multiobjective clustering techniques are described in brief.

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Notes

  1. 1.

    http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Expectation_Maximization_%28EM%29

  2. 2.

    http://www.dbs.informatik.uni-muenchen.de/Forschung/KDD/Clustering/

  3. 3.

    http://en.wikipedia.org/wiki/DBSCAN

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  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  2. Dept. of Computer Science, Indian Institute of Technology, Patna, India

    Sriparna Saha

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Bandyopadhyay, S., Saha, S. (2013). Clustering Algorithms. In: Unsupervised Classification. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32451-2_4

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