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On Some Fuzzy Clustering for Series Data

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14376))

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Abstract

Various fuzzification techniques have been applied to clustering algorithms for vectorial data, such as Yang-type fuzzification and extended q-divergence-regularization, whereas only a few such techniques have been applied to fuzzy clustering algorithms for series data. In this regard, this study presents four fuzzy clustering algorithms for series data. The first two algorithms are obtained by penalizing each optimization problem in the two conventional algorithms: Bezdek-type fuzzy dynamic-time-warping (DTW) c-means and Bezdek-type fuzzy c-shape, with the cluster-size controller fixed. The other two algorithms are obtained from a conventional algorithm, q-divergence-based fuzzy DTW c-means or q-divergence-based fuzzy c-shape, by distinguishing two fuzzificators for membership from those for cluster-size controllers. Numerical experiments are conducted to evaluate the performance of the proposed algorithms.

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Authors and Affiliations

  1. Shibaura Institute of Technology, 3-7-5 Toyosu, Koto, Tokyo, 135-8548, Japan

    Yuto Suzuki & Yuchi Kanzawa

Authors
  1. Yuto Suzuki
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  2. Yuchi Kanzawa
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Corresponding author

Correspondence to Yuchi Kanzawa .

Editor information

Editors and Affiliations

  1. Osaka Metropolitan University, Sakai, Osaka, Japan

    Katsuhiro Honda

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Bac Le

  3. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Van-Nam Huynh

  4. Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

  5. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Youji Kohda

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Suzuki, Y., Kanzawa, Y. (2023). On Some Fuzzy Clustering for Series Data. In: Honda, K., Le, B., Huynh, VN., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14376. Springer, Cham. https://doi.org/10.1007/978-3-031-46781-3_18

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  • DOI: https://doi.org/10.1007/978-3-031-46781-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46780-6

  • Online ISBN: 978-3-031-46781-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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