FRBC: A fuzzy rule-based clustering algorithm

IEEE Transactions on Fuzzy Systems

Volumn 19, Issue 5, 2011, Pages 960-971

  Mansoori, Eghbal G ^a  

^a SHIRAZ UNIVERSITY   (Iran)

Author keywords

Clustering; clustering algorithm; fuzzy clustering; fuzzy rule based classifier

Indexed keywords

BENCHMARK DATASETS; CLASSIFICATION OF DATA; CLUSTERING; DATA PATTERNS; FUZZY RULE-BASED CLASSIFIER; HUMAN USERS; INITIAL POSITION; INTERPRETABLE FUZZY RULES; RULE BASED; SUPERVISED CLASSIFICATION; UNSUPERVISED CLUSTER;

CLUSTER ANALYSIS; FUZZY CLUSTERING; FUZZY RULES;

CLUSTERING ALGORITHMS;

EID: 80053643343     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2011.2158651     Document Type: Article

References (42)

1. E. Backer and A. Jain, "A clustering performance measure based on fuzzy set decomposition," IEEE Trans. Pattern Anal.Mach. Intell., vol. PAMI-3, no. 1, pp. 66-75, Jan. 1981. (Pubitemid 11479062)
2. A. Jain and R. Dubes, Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice-Hall, 1988.
3. B. Everitt, S. Landau, and M. Leese, Cluster Analysis. London, U.K: Arnold, 2001.
4. A. Rauber, J. Paralic, and E. Pampalk, "Empirical evaluation of clustering algorithms," J. Inf. Org. Sci., vol. 24, no. 2, pp. 195-209, 2000.
5. A. Jain, M. Murty, and P. Flynn, "Data clustering: A review," ACM Comput. Surv., vol. 31, no. 3, pp. 264-323, 1999.
6. P. Berkhin. (2001). Survey of Clustering Data Mining Techniques. [Online]. Available: http://www.accrue.com/products/rp-cluster-review.pdf
7. R. Xu and D. Wunsch, "Survey of clustering algorithms," IEEE Trans. Neural Netw., vol. 16, no. 3, pp. 645-678, May 2005. (Pubitemid 40718010)
8. R.Yager andD. Filev, "Approximate clustering via themountain method," IEEE Trans. Syst.,Man, Cybern., vol. 24, no. 8, pp. 1279-1284, Aug. 1994.
9. R. Tibshirani, G. Walther, and T. Hastie, "Estimating the number of clusters in a data set via the gap statistic," J. R. Statist. Soc. B, vol. 63, no. 2, pp. 411-423, 2001.
10. L. Wang, C. Leckie, K. Ramamohanarao, and J. Bezdek, "Automatically determining the number of clusters in unlabeled data sets," IEEE Trans. Knowl. Data Eng., vol. 21, no. 3, pp. 335-350, Mar. 2009.
11. R. Dubes and A. K. Jain, "Validity studies in clustering methodologies," Pattern Recognit., vol. 11, pp. 235-254, 1979.
12. X. L. Xie and G. Beni, "A validity measure for fuzzy clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 8, pp. 841-847, Aug. 1991. (Pubitemid 21704193)
13. G. W. Milligan and M. C. Cooper, "An examination of procedures for determining the number of clusters in a data set," Psychometrika, vol. 50, no. 2, p. 159-179, Jun. 1985.
14. E. Dimitriadou, S. Dolnicar, and A. Weingessel, "An examination of indexes for determining the number of clusters in binary data set," Psychometrika, vol. 67, no. 3, pp. 137-160, Sep. 2002.
15. G. J. Klir and B. Yuan, "Fuzzy sets and fuzzy logic: Theory and Applications," in Pattern Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1995.
16. L. Zadeh, "Fuzzy sets," Inf. Control, vol. 8, pp. 338-353, 1965.
17. J. Yao, M. Dash, S. T. Tan, and H. Liu, "Entropy-based fuzzy clustering and fuzzy modeling," Fuzzy Sets Syst., vol. 113, no. 3, pp. 381-388, Aug. 2000.
18. A. Baraldi and P. Blonda, "A survey of fuzzy clustering algorithms for pattern recognition-Parts I and II," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 29, no. 6, pp. 778-801, Dec. 1999.
19. A. Geva, "Hierarchical unsupervised fuzzy clustering," IEEE Trans. Fuzzy Syst., vol. 7, no. 6, pp. 723-733, Dec. 1999. (Pubitemid 32212163)
20. A. Mirzaei andM. Rahmati, "A novel hierarchical-clustering- combination scheme based on fuzzy-similarity relations," IEEE Trans. Fuzzy Syst., vol. 18, no. 1, pp. 27-39, Feb. 2010.
21. X. Liu, W. Wang, and T. Chai, "The fuzzy clustering analysis based on AFS theory," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 35, no. 5, pp. 1013-1027, Oct. 2005. (Pubitemid 41444584)
22. K. Honda,A.Notsu, and H. Ichihashi, "Fuzzy PCA-guided robust k-means clustering," IEEE Trans. Fuzzy Syst., vol. 18, no. 1, pp. 67-79, Feb. 2010.
23. J. C. Bezdek, "Cluster validity with fuzzy sets," J. Cybern., vol. 3, pp. 58-71, 1974.
24. M. S. Yang, K. L. Wu, J. N. Hsieh, and J. Yu, "Alpha-cut implemented fuzzy clustering algorithms and switching regressions," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 3, pp. 588-603, Jun. 2008.
25. J. Yu and M. S. Yang, "Optimality test for generalized FCM and its application to parameter selection," IEEE Trans. Fuzzy Syst., vol. 13, no. 1, pp. 164-176, Feb. 2005. (Pubitemid 40319317)
26. J. Kolen and T. Hutcheson, "Reducing the time complexity of the fuzzy c-means algorithm," IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 263-267, Apr. 2002. (Pubitemid 34554868)
27. Y.Wang, C. Li, andY. Zuo, "A selectionmodel for optimal fuzzy clustering algorithm and number of clusters based on competitive comprehensive fuzzy evaluation," IEEE Trans. Fuzzy Syst., vol. 17, no. 3, pp. 568-577, Jun. 2009.
28. N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, "A possibilistic fuzzy c-means clustering algorithm," IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 517-530, Aug. 2005. (Pubitemid 41461446)
29. R. Krishnapuram and J.M.Keller, "Apossibilistic approach to clustering," IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98-110, May 1993.
30. S. Chatzis and T. Varvarigou, "Factor analysis latent subspace modeling and robust fuzzy clustering using t-distributions," IEEE Trans. Fuzzy Syst., vol. 17, no. 3, pp. 505-517, Jun. 2009.
31. L. Zhu, F. L. Chung, and S. Wang, "Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 3, pp. 578-591, Jun. 2009.
32. W. Wang and Y. Zhang, "On fuzzy cluster validity indices," Fuzzy Sets Syst., vol. 158, no. 3, pp. 2095-2117, 2007. (Pubitemid 47176685)
33. W. Pedrycz, V. Loia, and S. Senatore, "Fuzzy clustering with viewpoints," IEEE Trans. Fuzzy Syst., vol. 18, no. 2, pp. 274-284, Apr. 2010.
34. E. G. Mansoori, M. J. Zolghadri, and S. D. Katebi, "SGERD: A steadystate genetic algorithm for extracting fuzzy classification rules from data," IEEE Trans. Fuzzy Syst., vol. 16, no. 4, pp. 1061-1071, Aug. 2008.
35. H. Ishibuchi and T. Yamamoto, "Comparison of heuristic criteria for fuzzy rule selection in classification problems," Fuzzy Optim. DecisionMaking, vol. 3, no. 2, pp. 119-139, 2004.
36. E. G. Mansoori, M. J. Zolghadri, and S. D. Katebi, "A weighting function for improving fuzzy classification systems performance," Fuzzy Sets Syst., vol. 158, no. 5, pp. 583-591, 2007. (Pubitemid 46107690)
37. E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," Int. J. Man-Mach. Stud., vol. 7, no. 1, pp. 1-13, 1975.
38. F. Hoeppner, "Possibilistic shell clustering of template-based shapes," IEEE Trans. Fuzzy Syst., vol. 17, no. 4, pp. 777-793, Aug. 2009.
39. A. Asuncion and D. J. Newman. (2007). UCI Machine Learning Repository, Dept. Inf. Comput. Sci., Univ. Calif., Irvine [Online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html
40. J. Alcalá-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, and F.Herrera, "KEELdata-mining software tool:Data set repository, integration of algorithms and experimental analysis framework," J. Multiple-Valued Logic Soft Comput., vol. 17, no. 2-3, pp. 255-287, 2011.
41. R. Kohavi and F. Provost, "Glossary of terms," Mach. Learning, vol. 30, pp. 271-274, Feb./Mar. 1998.
42. B. Raskutti and C. Leckie, "An evaluation of criteria for measuring the quality of clusters," in Proc. Int. Joint Conf. Artif. Intell., 1999, pp. 905-910.