Study on multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering

Applied Soft Computing Journal

Volumn 16, Issue , 2014, Pages 89-101

  Zeng, Shan ^a   Tong, Xiaojun ^b   Sang, Nong ^c  

^a WUHAN POLYTECHNIC UNIVERSITY   (China)

^b WUHAN TEXTILE UNIVERSITY   (China)

^c HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Fuzzy C means algorithm; Lattice similarity; Multi center; Spectral clustering

Indexed keywords

ARTIFICIAL DATASETS; FUZZY C MEANS CLUSTERING; FUZZY C-MEANS ALGORITHMS; FUZZY MEMBERSHIP VALUES; LATTICE SIMILARITY; MULTI-CENTER; SPECTRAL CLUSTERING; TRANSITIVE CLOSURE;

COPYING; FUZZY CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 84891598128     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2013.11.020     Document Type: Article

References (22)

1. N.S. Mishra, S. Ghosh, and A. Ghosh Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images Applied Soft Computing 12 2012 2683 2692
2. F. Zhao, H.Q. Liu, and L.C. Jiao Spectral clustering with fuzzy similarity measure Digital Signal Processing 21 2011 701 709
3. M. Sabzekar, and M. Naghibzadeh Fuzzy C-means improvement using relaxed constraints support vector machines Applied Soft Computing 13 2013 881 890
4. K.R. Zalik Cluster validity index for estimation of fuzzy clusters of different sizes and densities Pattern Recognition 43 10 2010 3374 3390
5. Y.N. Wang, C.S. Li, and Y. Zuo A selection model for optimal fuzzy clustering algorithm and number of clusters based on competitive comprehensive fuzzy evaluation IEEE Transactions on Fuzzy Systems 17 3 2009 568 577
6. R.K. Brouwer, and A. Groenwold Modified fuzzy C-means for ordinal valued attributes with particle swarm for optimization Fuzzy Sets and Systems 161 13 2010 1774 1789
7. J. Shi, and J. Malik Normalized cuts and image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 2000 888 905
8. U. Luxburg A Tutorial on Spectral Clustering, Technical Report 2006
9. H.Q. Liu, F. Zhao, and L.C. Jiao Fuzzy spectral clustering with robust spatial information for image segmentation Applied Soft Computing 12 2012 3636 3647
10. D. Chaudhuri, and B.B. Chaudhuri A novel multiseed nonhierarchical data clustering technique IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 27 5 1997 871 877 (Pubitemid 127770682)
11. C.W. Tao Unsupervised fuzzy clustering with multi-center clusters Fuzzy Sets and Systems 128 2002 305 322 (Pubitemid 34531608)
12. E. Martin, and H.P. Kriegel et al. A Density based algorithm for discovering clusters in large spatial databases with noise Proc. 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96) Portland, Oregon 1996
13. Y.N. Andrew, I.J. Michael, and W. Yair On spectral clustering: analysis and algorithm Advances in Neural Information Processing Systems 2002 MIT Press Cambridge, MA 849 856
14. J. Yu, and Q.S. Cheng Fuzzy clustering methods search optimal number of clusters Science in China (E) 32 2 2002 274 280
15. N.A. Erilli, U. Yolcu, and E. Eǧrioǧlu et al. Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks Expert Systems with Applications: An International Journal 38 3 2011 2248 2252
16. A. Asuncion, and D. Newman UCI Machine Learning Repository (Online) 2007 Available from: http://www.ics.uci.edu/∼mlearn/MLRepository.html
17. M.G. Gong, L.C. Jiao, and L.F. Bo et al. Image texture classification using a manifold distance based evolutionary clustering method Optical Engineering 47 7 2008 077201-1 077201-10
18. U. Maulik, and S. Bandyopadhyay Genetic algorithm-based clustering technique Pattern Recognition 33 9 2000 1455 1465
19. C. Carson, S. Belongie, and H. Greenspan et al. Blobworld: image segmentation using expectation-maximization and its application to image querying IEEE Transaction on Pattern Analysis and Machine Intelligence 24 8 2002 1026 1038
20. S. Basu, M. Bilenko, and R.J. Mooney A probabilistic framework for semi-supervised clustering Proc. ACM SIGKDD 10th International Conference on Knowledge Discovery and Data Mining 2004 ACM Press New York 59 68 (Pubitemid 40114916)
21. W. Tang, H. Xiong, S. Zhong, and J. Wu Enhancing semi-supervised clustering: a feature projection perspective Proc. ACM SIGKDD 13th International Conference on Knowledge Discovery and Data Mining 2007 ACM Press New York 707 716 (Pubitemid 350229256)
22. F. Wang, and C.S. Zhang Robust self-tuning semi-supervised learning Neurocomputing 70 2007 2931 2939 (Pubitemid 47308608)