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A Study of Fuzzy Clustering to Archetypal Analysis

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Intelligent Data Engineering and Automated Learning – IDEAL 2018 (IDEAL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11315))

Abstract

This paper presents a comparative study between a method for fuzzy clustering which retrieves pure individual types from data, the fuzzy clustering with proportional membership (FCPM), and an archetypal analysis algorithm based on Furthest-Sum approach (FS-AA). A simulation study comprising 82 data sets is conducted with a proper data generator, FCPM-DG, whose goal is twofold: first, to analyse the ability of archetypal clustering algorithm to recover Archetypes from data of distinct dimensionality; second, to analyse robustness of FCPM and FS-AA algorithms to outliers. The effectiveness of these algorithms are yet compared on clustering 12 diverse benchmark data sets from machine learning. The evaluation conducted with five primer unsupervised validation indices shows the good quality of the clustering solutions.

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Acknowledgments

This research is supported by FCT/MCTES, NOVA LINCS (UID/CEC/04516/2013). The authors are thankful to the anonymous reviewers for their insightful and constructive comments that allowed to improve the paper.

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Authors and Affiliations

  1. Computer Science Department and NOVA LINCS, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisbon, Portugal

    Gonçalo Sousa Mendes & Susana Nascimento

Authors
  1. Gonçalo Sousa Mendes
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  2. Susana Nascimento
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Corresponding author

Correspondence to Susana Nascimento .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Rm 209, Building B, Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Mendes, G.S., Nascimento, S. (2018). A Study of Fuzzy Clustering to Archetypal Analysis. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_28

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  • DOI: https://doi.org/10.1007/978-3-030-03496-2_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03495-5

  • Online ISBN: 978-3-030-03496-2

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