Auto-Selection of Cluster Number in MMMs-Induced Fuzzy Co-Clustering
Fuzzy co-clustering induced by multinomial mixture models (FCCMM) is an effective method for analyzing such cooccurrence information data as document-keyword frequencies, but often suffers from the cluster validation problem due to a priori selection of cluster numbers. In this paper, a modified mod...
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| Veröffentlicht in: | Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 2020/04/15, Vol.32(2), pp.678-685 |
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| container_end_page | 685 |
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| container_issue | 2 |
| container_start_page | 678 |
| container_title | Journal of Japan Society for Fuzzy Theory and Intelligent Informatics |
| container_volume | 32 |
| creator | UBUKATA, Seiki YANAGISAWA, Kazuki NOTSU, Akira HONDA, Katsuhiro |
| description | Fuzzy co-clustering induced by multinomial mixture models (FCCMM) is an effective method for analyzing such cooccurrence information data as document-keyword frequencies, but often suffers from the cluster validation problem due to a priori selection of cluster numbers. In this paper, a modified model of robust cluster number selection in Gaussian mixture models is proposed, where the optimal number of clusters are automatically extracted in FCCMM through rejection of unnecessary clusters considering a novel penalty on cluster volumes. |
| doi_str_mv | 10.3156/jsoft.32.2_678 |
| format | Article |
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| identifier | ISSN: 1347-7986 |
| ispartof | Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 2020/04/15, Vol.32(2), pp.678-685 |
| issn | 1347-7986 1881-7203 |
| language | eng |
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| source | J-STAGE Free; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | cluster number estimation Clustering co-clustering fuzzy clustering Probabilistic models robust EM algorithm |
| title | Auto-Selection of Cluster Number in MMMs-Induced Fuzzy Co-Clustering |
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