Fuzzy co-clustering induced by q-multinomial mixture models

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

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060344
DOIs
Publication statusPublished - 2017 Aug 23
Event2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Naples, Italy
Duration: 2017 Jul 92017 Jul 12

Other

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Fuzzy co-clustering induced by q-multinomial mixture models'. Together they form a unique fingerprint.

  • Cite this

    Kanzawa, Y. (2017). Fuzzy co-clustering induced by q-multinomial mixture models. In 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 [8015398] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2017.8015398