New fuzzy c-means clustering model based on the data weighted approach

Data and Knowledge Engineering

Volumn 69, Issue 9, 2010, Pages 881-900

  Tang, Chenglong ^a   Wang, Shigang ^a   Xu, Wei ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Data weighted approach; Exponent impact factor; Fuzzy clustering; Influence exponent; Outliers mining

Indexed keywords

CLUSTER PROTOTYPE; CLUSTERING APPROACH; CLUSTERING PROCESS; CLUSTERING QUALITY; CLUSTERING RESULTS; DATA POINTS; DATA SETS; DATA WEIGHTED APPROACH; EXPONENT IMPACT FACTOR; FUZZY ALGORITHMS; FUZZY C MEANS CLUSTERING; FUZZY MEMBERSHIP; IMPACT FACTOR; INTERNAL CONNECTIVITY; LARGE SCALE DATA; NEW MODEL; NUMERICAL EXPERIMENTS; WEIGHTED FUZZY C-MEANS;

CLUSTERING ALGORITHMS; FUZZY CLUSTERING; FUZZY SETS; FUZZY SYSTEMS; INDEXING (OF INFORMATION);

COMPUTATIONAL EFFICIENCY;

EID: 77955271543     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2010.05.001     Document Type: Article

References (25)

1. Micheline Kamber, and Jiawei Han Data Mining: Concepts and Techniques 2nd edition 2005 Elsevier
2. Matthew Eric Otey, Amol Ghoting, and Spinvasan Parthasarathy Fast distributed outlier detection in mixed-attribute datasets Data Mining and Knowledge Discovery 12 2-3 2006 203 228
3. J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Plenum New York
4. R. Krishnapuram, and J.M. Keller A possibilistic approach to clustering IEEE Transactions on Fuzzy Systems 1 1993 98 110
5. M. Barni, V. Cappellini, and A. Mecocci Comments on "A possibilistic approach to clustering" IEEE Transactions on Fuzzy Systems 4 3 1996 393 396
6. N.R. Pal, K. Pal, and J.C. Bezdek A mixed c-means clustering model Proc. of 1997 IEEE Int. Conf. on Fuzzy Systems Vol. 1 1997 11 21
7. N.R. Pal, K. Pal, J.M. Keller, and J.C. Bezdek A possibilistic fuzzy c-means clustering algorithm IEEE Transactions on Fuzzy Systems 13 4 2005 517 530
8. Jacek M. Leski Generalized weighted conditional fuzzy clustering Transactions on Fuzzy Systems 11 6 2003 709 715
9. V. Torra, and S. Miyamoto On the use of variable-size fuzzy clustering for classification LNAI 3885 2006 362 371 (Pubitemid 43990504)
10. Wang Xi-Zhao, Wang Ya-Dong, and Wang Li-Juan Improving fuzzy c-means clustering based on feature-weight learning Pattern Recognition Letters 25 10 2004 1123 1132
11. Christian Borgelt Feature weighting and feature selection in fuzzy clustering, Fuzzy Systems, 2008. FUZZ-IEEE 2008 2008IEEE International Conference on Computational Intelligence Hong Kong June 2008 838 844
12. Chieh Yuan Tsai, and Cheng-Chiu Chuang Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm Computational Statistics & Data Analysis 52 2008 4658 4672
13. Elaine Y. Chan, Wai Ki. Ching, Micheal K. Ng, and Joshua Z. Huang An optimization algorithm for clustering using weighted dissimilarity measures Pattern Recognition 37 5 2004 943 952
14. Sirichai Turmchokkasam, and Sanya Mitaim Effects of weights in weighted fuzzy c-means algorithm for room equalization at multiple locations 2006 IEEE International Conference on Fuzzy Systems Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16-21 2006 1468 1475
15. Luh Yen, Francois Fouss, and Christine Decaestecker Graph nodes clustering with the sigmoid commute-time kernel: a comparative study Data & Knowledge Engineering 68 3 2009 338 361
16. Cheng-Ru Lin, Ken-Hao Liu, and Ming-Syan Chen Dual clustering: integrating data clustering over optimization and constraint domains IEEE Transactions on Knowledge and Data Engineering 17 5 2005 628 637
17. Sagi Katz, and Ayellet Tal Hierarchical mesh decomposition using fuzzy clustering and cuts ACM Transactions on Graphics 22 3 2003 954 961
18. Nikhil R. Pal, and James C. Bezdek On cluster validity for the fuzzy c-means model IEEE Transactions on Fuzzy Systems 3 3 1995 370 378
19. M. Breunig, H. Kriegel, and R.T. Ng LOF: identifying density-based local outliers proceedings of ACM SIGMOD International Conference on Management of Data, Dallas 2000 ACM Press Texas 93 104
20. Amol Ghoting, Srinivasan Parthasarathy, and Matthew Eric Otey Fast mining of distance-based outliers in high-dimensional dataset Data Mining and Knowledge Discovery 16 2008 349 364
21. Xiao-Qing Weng, and Jun-Yi Shen Detecting outlier samples in multivariate time series dataset Knowledge-based System 21 2008 807 812
22. Giulia Bruno, and Paola Garza TOD: temporal outlier detection by using quasi-functional temporal dependencies Data & Knowledge Engineering 69 6 2010 619 639
23. R.N. Dave Characterization and detection of noise in clustering Pattern Recognition Letters 12 11 1991 657 664
24. E.E. Gustafson, and W.C. Kessel Fuzzy clustering with a fuzzy covariance matrix Proceedings of the IEEE Conference on Decision, Control, San Diego, California 1979 IEEE Press Piscataway, NJ 761 766
25. X.L. Xie, and G. Beni A validity index for fuzzy clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 13 8 1991 841 847