On an Multi-directional Searching Algorithm for Two Fuzzy Clustering Methods for Categorical Multivariate Data

Kazune Suzuki, Yuchi Kanzawa

研究成果: Conference contribution

抄録

Clustering for categorical multivariate data is an important task for summarizing co-occurrence information that consists of mutual affinity among objects and items. This work focus on two fuzzy clustering methods for categorical multivariate data. One of the serious limitations for these methods is the local optimality problem. In this work, an algorithm is proposed to address this issue. The proposed algorithm incorporates multiple token search generated from the eigen decomposition of the Hessian of the objective function. Numerical experiments using an artificial dataset shows that the proposed algorithm is valid.

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182-190
ページ数9
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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