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.
| Language | English |
|---|---|
| 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 | |
| State | 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 |
|---|---|
| Country | Italy |
| City | Naples |
| Period | 17/7/9 → 17/7/12 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Artificial Intelligence
- Applied Mathematics
Cite this
Fuzzy co-clustering induced by q-multinomial mixture models. / Kanzawa, Yuchi.
2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8015398.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fuzzy co-clustering induced by q-multinomial mixture models
AU - Kanzawa,Yuchi
PY - 2017/8/23
Y1 - 2017/8/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030147973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030147973&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE.2017.8015398
DO - 10.1109/FUZZ-IEEE.2017.8015398
M3 - Conference contribution
BT - 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -