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 language | English |
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Title of host publication | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509060344 |
DOIs | |
Publication status | Published - 2017 Aug 23 |
Event | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Naples, Italy Duration: 2017 Jul 9 → 2017 Jul 12 |
Other
Other | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 |
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Country | Italy |
City | Naples |
Period | 17/7/9 → 17/7/12 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Artificial Intelligence
- Applied Mathematics