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Clustering Methods

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Data Mining and Knowledge Discovery Handbook

Abstract

This chapter presents a tutorial overview of the main clustering methods used in Data Mining. The goal is to provide a self-contained review of the concepts and the mathematics underlying clustering techniques. The chapter begins by providing measures and criteria that are used for determining whether two objects are similar or dissimilar. Then the clustering methods are presented, divided into: hierarchical, partitioning, density-based, model-based, grid-based, and soft-computing methods. Following the methods, the challenges of performing clustering in large data sets are discussed. Finally, the chapter presents how to determine the number of clusters.

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Authors and Affiliations

  1. Department of Industrial Engineering, Tel-Aviv University, USA

    Lior Rokach & Oded Maimon

Authors
  1. Lior Rokach
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  2. Oded Maimon
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Editors and Affiliations

  1. Dept. of Industrial Engineering, Tel-Aviv University, 69978, Ramat-Aviv, Israel

    Oded Maimon  & Lior Rokach  & 

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Rokach, L., Maimon, O. (2005). Clustering Methods. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_15

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  • DOI: https://doi.org/10.1007/0-387-25465-X_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-24435-8

  • Online ISBN: 978-0-387-25465-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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