Abstract
Fuzzy co-clustering is a fundamental technique for summarizing the structural characteristics of cooccurrence information. In this chapter, following the brief introduction of fuzzy c-Means (FCM) clustering, FCM-induced fuzzy co-clustering model is reviewed with illustrative examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M.R. Anderberg, Cluster Analysis for Applications (Academic Press, New York, 1973)
A.K. Jain, R.C. Dubes, Algorithms for Clustering Data (Prentice Hall, Englewood Cliffs, NJ, 1988)
J.B. MacQueen, Some methods of classification and analysis of multivariate observations, in Proceeding of 5th Berkeley Symposium on Mathematics of Stats and Probability (1967), pp. 281–297
J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, 1981)
C.-H. Oh, K. Honda, H. Ichihashi: Fuzzy clustering for categorical multivariate data, in Proceeding of Joint 9th IFSA World Congress and 20th NAFIPS International Conference (2001), pp. 2154–2159
J.C. Dunn, Well separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95–104 (1974)
F. Höppner, F. Klawonn, R. Kruse, T. Runkler, Fuzzy Cluster Analysis (Wiley, 1999)
S. Miyamoto, H. Ichihashi, K. Honda, Algorithms for Fuzzy Clustering (Springer, 2008)
W. Wang, Y. Zhang, On fuzzy cluster validity indices. Fuzzy Sets Syst. 158, 2095–2117 (2007)
S. Miyamoto, M. Mukaidono, Fuzzy \(c\)-means as a regularization and maximum entropy approach, in Proceeding of the 7th International Fuzzy Systems Association World Congress, vol. 2. (1997), pp. 86–92
R.J. Hathaway, Another interpretation of the EM algorithm for mixture distributions. Stat. & Probab. Lett. 4, 53–56 (1986)
C.M. Bishop, Neural Networks for Pattern Recognition (Clarendon Press, 1995)
K. Rose, E. Gurewitz, G. Fox, A deterministic annealing approach to clustering. Pattern Recognit. Lett. 11, 589–594 (1990)
A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc., Series B, 39, 1–38 (1977)
S. Miyamoto, K. Umayahara, Fuzzy clustering by quadratic regularization, in Proceeding 1998 IEEE International Conference Fuzzy Systems and IEEE World Congress. Computational Intelligence, vol. 2. (1998) pp. 1394–1399
H. Ichihashi, K. Miyagishi, K. Honda, Fuzzy \(c\)-means clustering with regularization by K-L information, in Proceeding of 10th IEEE International Conference on Fuzzy Systems, vol. 2. (2001) pp. 924–927
K. Honda, H. Ichihashi, Regularized linear fuzzy clustering and probabilistic PCA mixture models. IEEE Trans. Fuzzy Systems 13(4), 508–516 (2005)
M.E. Tipping, C.M. Bishop, Mixtures of probabilistic principal component analysers. Neural Comput. 11(2), 443–482 (1999)
K. Honda, H. Ichihashi, Linear fuzzy clustering techniques with missing values and their application to local principal component analysis. IEEE Trans. Fuzzy Systems 12(2), 183–193 (2004)
R.J. Hathaway, J.C. Bezdek, Switching regression models and fuzzy clustering. IEEE Trans. on Fuzzy Systems 1(3), 195–204 (1993)
R.E. Quandt, A new approach to estimating switching regressions. J. Am. Stat. Assoc. 67, 306–310 (1972)
J.C. Bezdek, J. Keller, R. Krishnapuram, N.R. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (Kluwer, Boston, 1999)
G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)
V. Gupta, G.S. Lehal, A survey of text summarization extractive techniques. J. Emerg. Technol. Web Intell. 2(3), 258–268 (2010)
K. Honda, A. Notsu, H. Ichihashi, Collaborative filtering by sequential user-item co-cluster extraction from rectangular relational data. Int. J. Knowl. Eng. Soft Data Parad. 2(4), 312–327 (2010)
L. Rigouste, O. Cappé, F. Yvon, Inference and evaluation of the multinomial mixture model for text clustering. Inf. Process. Manag. 43(5), 1260–1280 (2007)
K. Sjölander, K. Karplus, M. Brown, R. Hughey, A. Krogh, I. Saira Mian, D. Haussler, Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology. Comput. Appl. Biosci. 12(4), 327–345 (1996)
K. Kummamuru, A. Dhawale, R. Krishnapuram: Fuzzy co-clustering of documents and keywords, in Proceeding 2003 IEEE International Conference Fuzzy Systems, vol. 2. (2003), pp. 772–777
K. Honda, S. Oshio, A. Notsu, FCM-type fuzzy co-clustering by K-L information regularization, in Proceeding of 2014 IEEE International Conference on Fuzzy Systems (2014), pp. 2505–2510
T. Kondo, Y. Kanzawa, Fuzzy clustering methods for categorical multivariate data based on \(q\)-divergence. J. Adv. Comput. Intell. Intell. Inform. 22(4), 524–536 (2018)
Y. Kanzawa, Bezdek-type fuzzified co-clustering algorithm. J. Adv. Comput. Intell. Intell. Inform. 19(6), 852–860 (2015)
K. Honda, S. Oshio, A. Notsu, Fuzzy co-clustering induced by multinomial mixture models. J. Adv. Comput. Intell. Intell. Inform. 19(6), 717–726 (2015)
T. Hofmann, Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)
T. Goshima, K. Honda, S. Ubukata, A. Notsu, Deterministic annealing process for pLSA-induced fuzzy co-clustering and cluster splitting characteristics. Int. J. Approx. Reason. 95, 185–193 (2018)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Chen, TC.T., Honda, K. (2020). Fuzzy Clustering and Fuzzy Co-clustering. In: Fuzzy Collaborative Forecasting and Clustering. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-22574-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-22574-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22573-5
Online ISBN: 978-3-030-22574-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)