Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering

Katsuhiro Honda; Takuya Sako; Seiki Ubukata; Akira Notsu

doi:10.20965/jaciii.2018.p0585

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JACIII Vol.22 No.5 pp. 585-592

doi: 10.20965/jaciii.2018.p0585

(2018)

Paper:

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Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering

Katsuhiro Honda, Takuya Sako, Seiki Ubukata, and Akira Notsu

Osaka Prefecture University
1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

Received:

March 13, 2018

Accepted:

April 16, 2018

Published:

September 20, 2018

Keywords:

co-clustering, data structure visualization, eigen-problem, cluster validation

Abstract

Co-cluster extraction is a basic approach for summarization of cooccurrence information. This paper proposes a visual assessment technique for co-cluster structure analysis through cooccurrence-sensitive ordering, which realizes the hybrid concept of the coVAT algorithm and distance-sensitive ordering in relational data clustering. Object-item cooccurrence information is first enlarged into an (object + item) × (object + item) cooccurrence data matrix, and then, cooccurrence-sensitive ordering is performed through spectral ordering of the enlarged matrix. Additionally, this paper also consider the intuitive validation of co-cluster structures considering cluster crossing curves, which was adopted in cluster validation with distance-sensitive ordering. The characteristic features of the proposed approach are demonstrated through several numerical experiments including application to social analysis of Japanese prefectural statistics.

Visual image of co-cluster structure

Cite this article as:

K. Honda, T. Sako, S. Ubukata, and A. Notsu, “Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.5, pp. 585-592, 2018.

Data files:

References

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[3] T. Hofmann, “Unsupervised learning by probabilistic latent semantic analysis,” Machine Learning, Vol.42, No.1-2, pp. 177-196, 2001.
[4] 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.
[5] K. Kummamuru, A. Dhawale, and R. Krishnapuram, “Fuzzy co-clustering of documents and keywords,” Proc. 2003 IEEE Int. Conf. Fuzzy Systems, Vol.2, pp. 772-777, 2003.
[6] K. Honda, S. Oshio, and A. Notsu, “Fuzzy co-clustering induced by multinomial mixture models,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.6, pp. 717-726, 2015.
[7] W. Wang and Y. Zhang, “On fuzzy cluster validity indices,” Fuzzy Sets and Systems, Vol.158, pp. 2095-2117, 2007.
[8] J. C. Bezdek and R. J. Hathaway, “VAT: A tool for visual assessment of (cluster) tendency,” Proc. Int. Joint Conf. Neural Networks, pp. 2225-2230, 2002.
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[10] C. Ding and X. He, “Linearized cluster assignment via spectral ordering,” Proc. of Int. Conf. Machine Learning, pp. 233-240, 2004.
[11] K. Honda, T. Sako, S. Ubukata, and A. Notsu, “Visual assessment of co-cluster structure through cooccurrence-sensitive ordering,” Proc. of Joint 17th World Congress of Int. Fuzzy Systems Association and 9th Int. Conf. on Soft Computing and Intelligent Systems, paper #50, pp. 1-6, 2017.
[12] 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.
[13] 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.
[14] K. Honda, A. Notsu, and H. Ichihashi, “An efficient algorithm for collaborative filtering based on sequential user-item co-cluster extraction,” Proc. of Joint 5th Int. Conf. on Soft Computing and Intelligent Systems and 11th Int. Symp. on Advanced Intelligent Systems, pp. 4-8, 2010.
[15] R. A. Olshen and B. Rajaratnam, “Successive normalization of rectangular arrays,” Annals of Statistics, Vol.38, No.3, pp. 1638-1664, 2010.
[16] K. Honda, N. Yamamoto, S. Ubukata, and A. Notsu, “Noise rejection in mmms-induced fuzzy co-clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.7, pp. 1144-1151, 2017.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] L. Rigouste, O. Cappé, 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.

[2] [2] K. Sjölander, K. Karplus, M. Brown, R. Hughey, A. Krogh, I. Saira Mian, and D. Haussler, “Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology,” Computer Applications in the Biosciences, Vol.12, No.4, pp. 327-345, 1996.

[3] [3] T. Hofmann, “Unsupervised learning by probabilistic latent semantic analysis,” Machine Learning, Vol.42, No.1-2, pp. 177-196, 2001.

[4] [4] 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.

[5] [5] K. Kummamuru, A. Dhawale, and R. Krishnapuram, “Fuzzy co-clustering of documents and keywords,” Proc. 2003 IEEE Int. Conf. Fuzzy Systems, Vol.2, pp. 772-777, 2003.

[6] [6] K. Honda, S. Oshio, and A. Notsu, “Fuzzy co-clustering induced by multinomial mixture models,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.6, pp. 717-726, 2015.

[7] [7] W. Wang and Y. Zhang, “On fuzzy cluster validity indices,” Fuzzy Sets and Systems, Vol.158, pp. 2095-2117, 2007.

[8] [8] J. C. Bezdek and R. J. Hathaway, “VAT: A tool for visual assessment of (cluster) tendency,” Proc. Int. Joint Conf. Neural Networks, pp. 2225-2230, 2002.

[9] [9] J. C. Bezdek, R. J. Hathaway, and J. M. Huband, “Visual assessment of clustering tendency for rectangular dissimilarity matrices,” IEEE Trans. on Fuzzy Systems, Vol.15, No.5, pp. 890-903, 2007.

[10] [10] C. Ding and X. He, “Linearized cluster assignment via spectral ordering,” Proc. of Int. Conf. Machine Learning, pp. 233-240, 2004.

[11] [11] K. Honda, T. Sako, S. Ubukata, and A. Notsu, “Visual assessment of co-cluster structure through cooccurrence-sensitive ordering,” Proc. of Joint 17th World Congress of Int. Fuzzy Systems Association and 9th Int. Conf. on Soft Computing and Intelligent Systems, paper #50, pp. 1-6, 2017.

[12] [12] 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.

[13] [13] 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.

[14] [14] K. Honda, A. Notsu, and H. Ichihashi, “An efficient algorithm for collaborative filtering based on sequential user-item co-cluster extraction,” Proc. of Joint 5th Int. Conf. on Soft Computing and Intelligent Systems and 11th Int. Symp. on Advanced Intelligent Systems, pp. 4-8, 2010.

[15] [15] R. A. Olshen and B. Rajaratnam, “Successive normalization of rectangular arrays,” Annals of Statistics, Vol.38, No.3, pp. 1638-1664, 2010.

[16] [16] K. Honda, N. Yamamoto, S. Ubukata, and A. Notsu, “Noise rejection in mmms-induced fuzzy co-clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.7, pp. 1144-1151, 2017.