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Evolutionary Clustering and Community Detection

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Handbook of Evolutionary Machine Learning

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

This chapter provides a formal definition of the problem of cluster analysis, and the related problem of community detection in graphs. Building on the mathematical definition of these problems, we motivate the use of evolutionary computation in this setting. We then review previous work on this topic, highlighting key approaches regarding the choice of representation and objective functions, as well as regarding the final process of model selection. Finally, we discuss successful applications of evolutionary clustering and the steps we consider necessary to encourage the uptake of these techniques in mainstream machine learning.

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Notes

  1. 1.

    Cluster validity indices can be external or internal, depending on whether or not they depend on knowledge of the correct partition (ground truth) to determine solution quality.

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Author information

Authors and Affiliations

  1. University of Manchester, Manchester, UK

    Julia Handl

  2. Cinvestav, Campus Tamaulipas, Km. 5.5 carretera Cd. Victoria-Soto La Marina, Cd. Victoria, 87130, Tamaulipas, Mexico

    Mario Garza-Fabre

  3. Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000, Lille, France

    Adán José-García

Authors
  1. Julia Handl
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  2. Mario Garza-Fabre
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  3. Adán José-García
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Correspondence to Julia Handl .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

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Handl, J., Garza-Fabre, M., José-García, A. (2024). Evolutionary Clustering and Community Detection. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_6

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