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Introduction to Clustering

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Partitional Clustering via Nonsmooth Optimization

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

In this chapter, we first define the commonly used tasks and terminologies in data analysis and show the importance of data clustering. Then we give a mathematical formulation of the clustering problem and explain the frequently used similarity measures. Finally, we provide a short survey on different types of existing clustering algorithms and describe some of the most popular applications of data clustering.

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Authors and Affiliations

  1. School of Science, Engineering & Information Technology, Federation University Australia, Ballarat, VIC, Australia

    Adil M. Bagirov & Sona Taheri

  2. Department of Mathematics and Statistics, University of Turku, Turku, Finland

    Napsu Karmitsa

Authors
  1. Adil M. Bagirov
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  2. Napsu Karmitsa
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  3. Sona Taheri
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M. Bagirov, A., Karmitsa, N., Taheri, S. (2020). Introduction to Clustering. In: Partitional Clustering via Nonsmooth Optimization. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-37826-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-37826-4_1

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