Abstract
Fuzzy co-clustering is a powerful tool for summarizing co-occurrence information while some intrinsic knowledge on meaningful items may be concealed by the dominant items shared by multiple clusters. In this paper, the conventional fully exclusive item partition model is modified such that exclusive penalties are forced only on some selected items. Its advantages are demonstrated through two numerical experiments. In a document clustering task, the proposed model is utilized for emphasizing cluster-wise meaningful keywords, which are useful for effectively summarizing document clusters. In an unsupervised classification task, the classification quality is improved by efficiently selecting promising items based on the item-wise single penalization test.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press (1981)
Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for Fuzzy Clustering. Springer (2008)
Hathaway, R.J., Davenport, J.W., Bezdek, J.C.: Relational duals of the \(c\)-means clustering algorithms. Pattern Recognition 22(2), 205–212 (1989)
Hathaway, R.J., Bezdek, J.C.: Nerf \(c\)-means: non-Euclidean relational fuzzy clustering. Pattern Recognition 27(3), 429–437 (1994)
Oh, C.-H., Honda, K., Ichihashi, H.: Fuzzy clustering for categorical multivariate data. In: Proc. of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 2154–2159 (2001)
Miyamoto, S., Mukaidono, M.: Fuzzy \(c\)-means as a regularization and maximum entropy approach. In: Proc. of the 7th International Fuzzy Systems Association World Congress, vol. 2, pp. 86–92 (1997)
Rigouste, L., Cappé, O., Yvon, F.: Inference and evaluation of the multinomial mixture model for text clustering. Information Processing and Management 43(5), 1260–1280 (2007)
Honda, K., Oshio, S., Notsu, A.: FCM-type fuzzy co-clustering by K-L information regularization. In: Proc. of 2014 IEEE International Conference on Fuzzy Systems, pp. 2505–2510 (2014)
Honda, K., Oshio, S., Notsu, A.: Fuzzy co-clustering induced by multinomial mixture models. Journal of Advanced Computational Intelligence and Intelligent Informatics 19 (to appear, 2015)
Honda, K., Ichihashi, H.: Regularized linear fuzzy clustering and probabilistic PCA mixture models. IEEE Transactions on Fuzzy Systems 13(4), 508–516 (2005)
Honda, K., Oh, C.-H., Notsu, A.: Exclusive condition on item partition in fuzzy co-clustering based on K-L information regularization. In: Proc. Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems, pp. 1413–1417 (2014)
Honda, K., Notsu, A., Ichihashi, H.: Fuzzy PCA-guided robust \(k\)-means clustering. IEEE Transactions on Fuzzy Systems 18(1), 67–79 (2010)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Nakano, T., Honda, K., Ubukata, S., Notsu, A. (2015). MMMs-Induced Fuzzy Co-clustering with Exclusive Partition Penalty on Selected Items. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-25135-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25134-9
Online ISBN: 978-3-319-25135-6
eBook Packages: Computer ScienceComputer Science (R0)