Fuzzy co-clustering induced by q-multinomial mixture models

研究成果: Conference contribution

抄録

In this study, a new fuzzy co-clusterins algorithm based on a q-multinomial mixture model is proposed. A conventional fuzzy co-clustering model was constructed by fuzzifying a multinomial mixture model (MMM) via regularizing Kullback-Leibler divergence appearing in a pseudo likelihood of an MMM. Furthermore, a q-multinomial distribution was formulated, which acts as the Tsallis statistical counter for multinomial distributions in standard statistics. The proposed algorithm is constructed by fuzzifying a q-multinomial mixture model, by means of regularizing q-divergence appearing in a pseudo likelihood of the model. The proposed algorithm not only reduces into the q-multinomial mixture model, but also reduces into conventional fuzzy co-clustering models with specified sets of parameter values. In numerical experiments, the properties of the membership of the proposed method are observed.

本文言語English
ホスト出版物のタイトル2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509060344
DOI
出版ステータスPublished - 2017 8 23
イベント2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Naples, Italy
継続期間: 2017 7 92017 7 12

出版物シリーズ

名前IEEE International Conference on Fuzzy Systems
ISSN(印刷版)1098-7584

Other

Other2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
国/地域Italy
CityNaples
Period17/7/917/7/12

ASJC Scopus subject areas

  • ソフトウェア
  • 理論的コンピュータサイエンス
  • 人工知能
  • 応用数学

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