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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings
PublisherSpringer Verlag
Pages89-102
Number of pages14
Volume10571 LNAI
ISBN (Print)9783319674216
DOIs
Publication statusPublished - 2017
Event14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017 - Kitakyushu, Japan
Duration: 2017 Oct 182017 Oct 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10571 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017
CountryJapan
CityKitakyushu
Period17/10/1817/10/20

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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