On fuzzy clustering for categorical multivariate data induced by polya mixture models

研究成果: Conference contribution

抜粋

In this paper, three fuzzy clustering models for categorical multivariate data are proposed based on the Polya mixture model and q-divergence. A conventional fuzzy clustering model for categorical multivariate data is constructed by fuzzifying a multinomial mixture model (MMM) via regularizing Kullback-Leibler (KL) divergence appearing in a pseudo likelihood of an MMM, whereas MMM is extended to a Polya mixture model (PMM) and no fuzzy counterpart to PMM is proposed. The first proposed model is constructed by fuzzifying PMM, by means of regularizing KL-divergence appearing in a pseudo likelihood of the model. The other two models are derived by modifying the first proposed algorithm, which is based on the fact that one of the three fuzzy clustering models for vectorial data is similar to the first proposed model, and that another fuzzy clustering model for vectorial data can connect the other two fuzzy clustering models for vectorial data based on q-divergence. In numerical experiments, the properties of the membership of the proposed methods were observed using an artificial dataset.

元の言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings
出版者Springer Verlag
ページ89-102
ページ数14
10571 LNAI
ISBN(印刷物)9783319674216
DOI
出版物ステータスPublished - 2017
イベント14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017 - Kitakyushu, Japan
継続期間: 2017 10 182017 10 20

出版物シリーズ

氏名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10571 LNAI
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Other

Other14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017
Japan
Kitakyushu
期間17/10/1817/10/20

    フィンガープリント

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

これを引用

Kanzawa, Y. (2017). On fuzzy clustering for categorical multivariate data induced by polya mixture models. : Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings (巻 10571 LNAI, pp. 89-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻 10571 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-67422-3_9