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

Paper:

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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

Received:
May 19, 2015
Accepted:
August 18, 2015
Published:
November 20, 2015
Creative Commons License
Keywords:
fuzzy clustering, co-clustering, exclusive partition, collaborative filtering
Abstract
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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References
  1. [1] E. Oja, A. Ilin, J. Luttinen, and Z. Yang, “Linear expansions with nonlinear cost functions: modeling, representation, and partitioning,” 2010 IEEE World Congress on Computational Intelligence, Plenary and Invited Lectures, pp.105-123, 2010.
  2. [2] Z. Yang and E. Oja, “Linear and nonlinear projective nonnegative matrix factorization,” IEEE Trans. on Neural Networks, Vol.21, No.5, pp. 734-749, 2010.
  3. [3] K. Honda, A. Notsu, and H. Ichihashi, “Collaborative filtering by sequential user-item co-cluster extraction from rectangular relational Data,” Int. J. of Knowledge Engineering and Soft Data Paradigms, Vol.2, No.4, pp. 312-327, 2010.
  4. [4] L. Rigouste, O. Cappée, and F. Yvon, “Inference and evaluation of the multinomial mixture model for text clustering,” Information Processing and Management, Vol.43, No.5, pp. 1260-1280, 2007.
  5. [5] C.-H. Oh, K. Honda, and H. Ichihashi, “Fuzzy clustering for categorical multivariate data,” Proc. of Joint 9th IFSA World Congress and 20th NAFIPS Int. Conf., pp. 2154-2159, 2001.
  6. [6] K. Honda, S. Oshio, and A. Notsu, “FCM-type fuzzy co-clustering by K-L information regularization,” Proc. of 2014 IEEE Int. Conf. on Fuzzy Systems, pp. 2505-2510, 2014.
  7. [7] K. Honda, S. Oshio, and A. Notsu, “Fuzzy co-clustering induced by multinomial mixture models,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.19, No.6, pp. 717-726, 2015.
  8. [8] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, 1981.
  9. [9] K. Honda, C.-H. Oh, and A. Notsu, “Exclusive condition on item partition in fuzzy co-clustering based on K-L information regularization,” Proc. Joint 7th Int. Conf. on Soft Computing and Intelligent Systems and 15th Int. Symp. on Advanced Intelligent Systems, pp. 1413-1417, 2014.
  10. [10] K. Tsuda, M. Minoh, and K. Ikeda, “Extracting straight lines by sequential fuzzy clustering,” Pattern Recognition Letters, Vol.17, pp. 643-649, 1996.
  11. [11] K. Honda, C.-H. Oh, Y. Matsumoto, A. Notsu, and H. Ichihashi, “Exclusive partition in FCM-type co-clustering and its application to collaborative filtering,” Int. J. of Computer Science and Network Security, Vol.12, No.12, pp. 52-58, 2012.
  12. [12] R. J. Hathaway, “Another interpretation of the EM algorithm for mixture distributions,” Statistics & Probability Letters, Vol.4, pp. 53-56, 1986.
  13. [13] K. Honda and H. Ichihashi, “Regularized linear fuzzy clustering and probabilistic PCA mixture models,” IEEE Trans. on Fuzzy Systems, Vol.13, No.4, pp. 508-516, 2005.
  14. [14] K. Honda, A. Notsu, and H. Ichihashi, “Fuzzy PCA-guided robust k-means clustering,” IEEE Trans. on Fuzzy Systems, Vol.18, No.1, pp. 67-79, 2010.
  15. [15] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing and Management, Vol.24, Iss.5, pp. 513-523, 1988.
  16. [16] J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gardon, and J. Riedl, “Grouplens: applying collaborative filtering to usenet news,” Communications of the ACM, Vol.40, No.3, pp. 77-87, 1997.
  17. [17] J. A. Swets, “Measuring the accuracy of diagnostic systems,” Science, Vol.240, No.4857, pp. 1285-1289, 1988.

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