On Some Fuzzy Clustering Algorithms for Time-Series Data

Mizuki Fujita, Yuchi Kanzawa

研究成果: Conference contribution

抄録

Various fuzzy clustering algorithms have been proposed for vectorial data. However, these methods have not been applied to time-series data. This paper presents three fuzzy clustering algorithms for time-series data based on dynamic time warping (DTW). The first algorithm involves Kullback–Leibler divergence regularization of the DTW k-means objective function. The second algorithm replaces the membership of the DTW k-means objective function with its power. The third algorithm involves q-divergence regularization of the objective function of the first algorithm. Theoretical discussion shows that the third algorithm is a generalization of the first and second algorithms, which is substantiated through numerical experiments.

本文言語English
ホスト出版物のタイトルIntegrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Proceedings
編集者Katsuhiro Honda, Tomoe Entani, Seiki Ubukata, Van-Nam Huynh, Masahiro Inuiguchi
出版社Springer Science and Business Media Deutschland GmbH
ページ169-181
ページ数13
ISBN(印刷版)9783030980177
DOI
出版ステータスPublished - 2022
イベント9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2022 - Ishikawa, Japan
継続期間: 2022 3月 182022 3月 19

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13199 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2022
国/地域Japan
CityIshikawa
Period22/3/1822/3/19

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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