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International Conference on Neural Information Processing

ICONIP 2021: Neural Information Processing pp 46–54Cite as

Multi-objective Clustering: A Data-Driven Analysis of MOCLE, MOCK and \(\varDelta \)-MOCK

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1516))

Abstract

We present a data-driven analysis of MOCK, \(\varDelta \)-MOCK, and MOCLE. These are three closely related approaches that use multi-objective optimization for crisp clustering. More specifically, based on a collection of 12 datasets presenting different proprieties, we investigate the performance of MOCLE and MOCK compared to the recently proposed \(\varDelta \)-MOCK. Besides performing a quantitative analysis identifying which method presents a good/poor performance with respect to another, we also conduct a more detailed analysis on why such a behavior happened. Indeed, the results of our analysis provide useful insights into the strengths and weaknesses of the methods investigated.

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Author information

Authors and Affiliations

  1. Department of Informatics, Federal University of Paraná, Curitiba, Brazil

    Adriano Kultzak, Cristina Y. Morimoto & Aurora Pozo

  2. LIFO, University of Orleans, Orleans, France

    Marcilio C. P. de Souto

Authors
  1. Adriano Kultzak
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  2. Cristina Y. Morimoto
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  3. Aurora Pozo
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  4. Marcilio C. P. de Souto
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Corresponding author

Correspondence to Cristina Y. Morimoto .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

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Kultzak, A., Morimoto, C.Y., Pozo, A., de Souto, M.C.P. (2021). Multi-objective Clustering: A Data-Driven Analysis of MOCLE, MOCK and \(\varDelta \)-MOCK. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-92307-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92306-8

  • Online ISBN: 978-3-030-92307-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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