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Link to original content: https://doi.org/10.1007/978-3-030-01418-6_68
Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data | SpringerLink
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Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data

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Artificial Neural Networks and Machine Learning – ICANN 2018 (ICANN 2018)

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Abstract

Symbolic Data Analysis provides suitable new types of variable that can take into account the variability present in the observed measurements. This paper proposes a partitioning fuzzy clustering algorithm for interval-valued data based on suitable adaptive Euclidean distance and entropy regularization. The proposed method optimizes an objective function by alternating three steps aiming to compute the fuzzy cluster representatives, the fuzzy partition, as well as relevance weights for the interval-valued variables. Experiments on synthetic and real datasets corroborate the usefulness of the proposed algorithm.

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Acknowledgment

The authors would like to thank CNPq and FACEPE (Brazilian agencies) for their financial support and the anonymous referees for their helpful suggestions.

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Authors and Affiliations

  1. Centro de Informática - CIn, Universidade Federal de Pernambuco, Recife, Brazil

    Sara Inés Rizo Rodríguez & Francisco de Assis Tenorio de Carvalho

Authors
  1. Sara Inés Rizo Rodríguez
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  2. Francisco de Assis Tenorio de Carvalho
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Corresponding author

Correspondence to Sara Inés Rizo Rodríguez .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Rodríguez, S.I.R., de Carvalho, F.d.A.T. (2018). Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_68

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  • DOI: https://doi.org/10.1007/978-3-030-01418-6_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01417-9

  • Online ISBN: 978-3-030-01418-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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