JACIII Vol.19 No.6 pp. 810-817
doi: 10.20965/jaciii.2015.p0810


Partially Exclusive Item Partition in MMMs-Induced Fuzzy Co-Clustering and its Effects in Collaborative Filtering

Katsuhiro Honda*, Takaya Nakano*, Chi-Hyon Oh**, Seiki Ubukata*, and Akira Notsu*

*Graduate School of Engineering, Osaka Prefecture University
1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

**Faculty of Liberal Arts and Sciences, Osaka University of Economics and Law
6-10 Gakuonji, Yao, Osaka 581-8511 Japan

May 19, 2015
August 18, 2015
November 20, 2015
fuzzy clustering, co-clustering, exclusive partition, collaborative filtering
The interpretability of fuzzy co-cluster partitions were shown to be improved by introducing exclusive penalties on both object and item memberships although the conventional fuzzy co-clustering adopted exclusive natures only on object memberships. In real applications, however, fully exclusive constraints may bring inappropriate influences to some items, and partially exclusive penalties should be forced reflecting the characteristics of each item. For example, in customer-product analysis, the degree of popularity of each product may be a measure of compatibility in multiple customer groups, and exclusive penalties should be forced only to some specific products. In this paper, the conventional exclusive constraint model is further modified by forcing exclusive penalties only to some selected items, and the effects of partially exclusive partition are demonstrated from the view points of not only partition quality but also collaborative filtering applicability. In a document-keyword analysis experiment, word class is shown to be useful for exclusively selecting keywords so that the interpretability of document cluster is improved. In a collaborative filtering experiment, the recommendation capability is demonstrated to be improved by considering intrinsic differences of popularity of each product.
Cite this article as:
K. Honda, T. Nakano, C. Oh, S. Ubukata, and A. Notsu, “Partially Exclusive Item Partition in MMMs-Induced Fuzzy Co-Clustering and its Effects in Collaborative Filtering,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.6, pp. 810-817, 2015.
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