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A Comparative Analysis Between Crisp and Fuzzy Data Clustering Approaches for Traditional and Bioinspired Algorithms

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

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Abstract

Partitional data clustering algorithms produce a relationship matrix between data and clusters, named membership matrix, which clusters can be treated as mutually exclusive (crisp) or not (fuzzy), according to data clustering approach used. Moreover, a partition obtained by a crisp algorithm can be fuzzified and so, the relationship between data and clusters be relaxed, such as in fuzzy data clustering approach. However, algorithms have your own heuristic and, iteratively, the behavior of a crisp algorithm can be different of that respective fuzzy version and, in addition, fuzzifying a crisp partition can produce different result in relation to crisp and fuzzy clustering algorithms. Therefore, this paper proposes a comparative analysis among results produced by fuzzy data clustering algorithms and their respective crisp versions and fuzzified partitions. The proposal is identify whether a fuzzy clustering algorithm can be replaced by its respective crisp with fuzzified partition, in terms of result quality. The experiments were performed to two traditional partitional algorithms and two bioinspired algorithms.

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Notes

  1. 1.

    https://www.gnu.org/software/octave/.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets.html.

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Acknowledgments

The authors thank Federal University of Grande Dourados (UFGD) for the financial support.

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Authors and Affiliations

  1. Federal University of Grande Dourados, Dourados, MS, Brazil

    Amanda Coradini & Alexandre Szabo

Authors
  1. Amanda Coradini
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  2. Alexandre Szabo
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Corresponding author

Correspondence to Alexandre Szabo .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Coradini, A., Szabo, A. (2020). A Comparative Analysis Between Crisp and Fuzzy Data Clustering Approaches for Traditional and Bioinspired Algorithms. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_29

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62364-7

  • Online ISBN: 978-3-030-62365-4

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