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Research Article

Fuzzy c-means clustering with conditional probability based K–L information regularization

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Pages 2699-2716 | Received 03 Aug 2019, Accepted 17 Mar 2021, Published online: 01 Apr 2021

References

  • Gan G, Ma C, Wu J. Data clustering: theory, algorithms and applications. Philadelphia: SIAM; 2007.
  • Yu Z, Au O C, Zou R, et al. An adaptive unsupervised approach toward pixel clustering and color image segmentation. Pattern Recognit. 2010;43:1889–1906.
  • Zarinbal M, Fazel Zarandi MH, Turksen IB. Relative entropy fuzzy c-means clustering. Inf Sci (Ny). 2014;260:74–97.
  • Gong M, Liang Y, Shi J, et al. Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process. 2013;22:573–584.
  • Ji Z, Xia Y, Sun Q, et al. Adaptive scale fuzzy local Gaussian mixture model for brain MR image segmentation. Neurocomputing. 2014;134:60–69.
  • Jacobs DW, Weinshall D, Gdalyahu Y. Classification with nonmetric distances: image retrieval and class representation. IEEE Trans Pattern Anal Mach Intell. 2000;22:583–600.
  • Amira O, et al. Weighted-capsule routing via a fuzzy gaussian model. Pattern Recognit Lett. 2020;138:424–430.
  • Zadeh L. Fuzzy sets. Inform Control. 1965;8:338–353.
  • Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern. 1973;3:32–57.
  • Bezdec JC. Pattern recognition with fuzzy Objective function algorithms. New York: Plenum Press; 1981.
  • MacQueen J. Some methods for classification and analysis of multivariate observations. in: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA: 1967; 1:281–297.
  • Krishnapuram R, Keller JM. A possibilistic approach to clustering. IEEE Trans Fuzzy Syst. 1993;1:98–110.
  • Pal NR, Pal K, Bezdek JC. A mixed c-means clustering model. In: IEEE Int Conf Fuzzy Syst. 1997;1:11–21.Spain
  • Pal NR, Pal K, Bezdek JC. A possibilistic fuzzy c-means clustering algorithm. In: IEEE Trans Fuzzy Syst. 2005;13:517–530.
  • Wang T, Hung WL. A generalized possibilistic approach to shell clustering of template-based shapes. J Stat Comput Simul. 2017;3:423–436.
  • Askari S, Montazerin N, Fazel Zarandi MH. Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data. Appl Soft Comput. 2017;53:262–283.
  • Miyamoto S, Mukaidono M. Fuzzy c-means as a regularization and maximum entropy approach. In Proc 7th Int Fuzzy Syst Assoc World Congress. 1997;2:86–92.
  • Li RP, Mukaidono M. A maximum-entropy approach to fuzzy clustering. In Proc Int Joint Conf 4th IEEE Int Conf Fuzzy/2nd Int Fuzzy Eng Symp (FUZZ/IEEE-IFES). 1995;4:2227–2232.Japan
  • Li RP, Mukaidono M. Gaussian clustering method based on maximum-fuzzy-entropy interpretation. Fuzzy Sets Syst. 1999;2:253–258.
  • Zhou J, Chen L, Chen CLP, et al. Fuzzy clustering with the entropy of attribute weights. Neurocomputing. 2016;198:125–134.
  • Askari S, Montazerin N, Fazel Zarandi MH, Hakimi E. Generalized entropy based possibilistic fuzzy C-Means for clustering noisy data and its convergence proof. Neurocomputing. 2017;219:186–202.
  • Ichihashi H, Miyagishi K, Honda K. Fuzzy c-means clustering with regularization by K-L information. In: 10th IEEE Int Conf Fuzzy Syst. 2001;2:924–927.
  • Ichihashi H, Honda K, Tani N. Gaussian mixture PDF approximation and fuzzy c-Means clustering with entropy regularization. In: Proc 4th Asian Fuzzy Systems Symp. 2000;217–221.Japan
  • Honda K, Ichihashi H. Regularized linear fuzzy clustering and probabilistic PCA mixture models. IEEE Trans Fuzzy Syst. 2005;13:508–516.
  • Ichihashi H, Honda K. Application of kernel trick to fuzzy c-Means with regularization by K–L information. J Adv Comput Intell Intell Inform. 2004;8:566–572.
  • Chatzis SP. A fuzzy c-means-type algorithm for clustering of data with mixed numeric and categorical attributes employing a probabilistic dissimilarity functional. Expert Syst Appl. 2011;38:8684–8689.
  • Honda K, Oshio S, Notsu A. FCM-type fuzzy co-clustering by K-L information regularization. in IEEE Int Conf Fuzzy Syst (FUZZ-IEEE). 2014;2505–2510. ASA-SIAM, 2007. doi:10.1109/fuzz-ieee.2014.6891747.
  • Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc: Ser B (Methodological). 1977;39(1):2505–2510.
  • Gray RM. Entropy and information theory. second ed. USA: Springer Science & Business Media; 2011.
  • Mclachlan GJ, Basford KE. Mixture models: inference and applications to clustering. New York: M. Dekker; 1988.
  • Bishop CM. Pattern recognition and machine learning. Vol. 29. New York: Springer; 2006;29:143–195.
  • Plummer MD, Lov a´sz L. Matching theory. Acadmiai Kiadó. 1986.
  • He, et al. Laplacian regularized gaussian mixture model for data clustering. IEEE Trans Knowledge Data Eng. 2010;23(9):1406–1418.
  • Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461–464.
  • Frank A, Asuncion A. UCI machine learning repository. 2011;15:22.
  • Alpert S, Galun M, Basri R, et al. Image segmentation by probabilistic bottom-Up aggregation and cue integration. Proc IEEE Conf Comput Vision Pattern Recognition. 2007. doi:10.1109/cvpr.2007.383017.

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