Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study

Fuzzy Sets and Systems

Volumn 161, Issue 4, 2010, Pages 522-543

  Graves, Daniel ^a   Pedrycz, Witold ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Classification rate; Evaluation criteria; Fuzzy C Means; Fuzzy clustering; Fuzzy kernel based clustering; Gustafson Kessel FCM; Kernels; Reconstruction error

Indexed keywords

CLASSIFICATION RATES; EVALUATION CRITERIA; FUZZY C MEAN; FUZZY C-MEANS; FUZZY KERNEL; GUSTAFSON-KESSEL; RECONSTRUCTION ERROR;

CLUSTER ANALYSIS; FUZZY RULES; FUZZY SYSTEMS; LEARNING ALGORITHMS; REPAIR;

FUZZY CLUSTERING;

EID: 72149122625     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2009.10.021     Document Type: Article

References (32)

1. A. Asuncion, D.J. Newman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA, 2007 [Online] Available: 〈http://archive.ics.uci.edu/beta/〉.
2. R. Babuska, P.J. van der Veen, U. Kaymak, Improved covariance estimation for Gustafson-Kessel clustering, in: Proc. of the IEEE Internat. Conf. on Fuzzy Systems, 2002, pp. 1081-1085.
3. Ben-Hur A., Horn D., Siegelmann H.T., and Vapnik V. Support vector clustering. Journal of Machine Learning Research 2 (2001) 125-137
4. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum, New York
5. S. Borer, W. Gerstner, A new kernel clustering algorithm, in: Proc. of the Ninth Internat. Conf. on Neural Information Processing, Vol. 5, 2002, pp. 2527-2531.
6. Bouchachia A., and Pedrycz W. Enhancement of fuzzy clustering by mechanisms of partial supervision. Fuzzy Sets and Systems 157 13 (2006) 1733-1759
7. Camastra F., and Verri A. A novel kernel method for clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 5 (2005) 801-805
8. Chiang J.H., and Hao P.Y. A new kernel-based fuzzy clustering approach: support vector clustering with cell growing. IEEE Transactions on Fuzzy Systems 11 4 (2003) 518-527
9. I.S. Dhillon, Y. Guan, B. Kulis, Kernel k-means spectral clustering and normalized cuts, in: Proc. of the 10th ACM Internat. Conf. on Knowledge Discovery and Data Mining, 2004, pp. 551-556.
10. M. Filippone, F. Camastra, F. Masulli, S. Rovetta, A survey of kernel and spectral methods for clustering, Pattern Recognition 41 (1) (2008) 176-190, doi: 10.1016/j.patcog.2007.05.018.
11. Girolami M. Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks 13 3 (2002) 780-784
12. Graves D., and Pedrycz W. Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-based FCM: a comparative study. Advances in Soft Computing 41 (2007) 140-149
13. D.E. Gustafson, W.C. Kessel, Fuzzy clustering with a fuzzy covariance matrix, in: IEEE Conf. on Decision Control Internat. 17th Symp. on Adaptive Processes, 1978, pp. 761-766.
14. Herbrich R. Learning Kernel Classifiers (2002), MIT Press, Cambridge, MA
15. Horn D. Clustering via Hilbert space. Physica A 302 1 (2001) 70-79
16. Kim D.W., Lee K.Y., Lee D., and Lee K.H. Evaluation of the performance of clustering algorithms kernel-induced feature space. Pattern Recognition 38 4 (2005) 607-611
17. Klawonn F., and Kruse R. Constructing a fuzzy controller from data. Fuzzy Sets and Systems 85 2 (1997) 177-193
18. Krishnapuram R., and Kim J. A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms. IEEE Transactions on Fuzzy Systems 7 4 (1999) 453-461
19. T. Li, S. Ma, M. Ogihara, Entropy-based criterion in categorical clustering, in: Proc. of the 21st Internat. Conf. on Machine Learning, Vol. 69, 2004, pp. 68-75.
20. Muller K.R., Mika S., Ratsch G., Tsuda K., and Scholkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12 2 (2001) 181-201
21. D.J. Newman, S. Hettich, C.L. Blake, C.J. Merz, UCI Repository of machine learning databases, University of California, School of Information and Computer Science, Irvine, CA, 1998 [Online] Available: 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉.
22. Pedrycz W. Knowledge-based Clustering (2005), Wiley, Hoboken, NJ
23. Qin J., Lewis D.P., and Noble W.S. Kernel hierarchical gene clustering from microarray expression data. Bioinformatics 19 16 (2003) 2097-2104
24. Scholkopf B., Smola A., and Muller K.R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 (1998) 1299-1319
25. Shen H., Yang J., Wang S., and Liu X. Attribute weighted Mercer kernel based fuzzy clustering algorithm for general non-spherical datasets. Soft Computing 10 11 (2006) 1061-1073
26. Z.D. Wu, W.X. Xie, J.P. Yu, Fuzzy c-means clustering algorithm based on kernel method, in: Proc. of the Internat. Conf. on Computational Intelligence and Multimedia Applications, 2003, pp. 49-54.
27. Z.R.Yang, Probabilistic mercer kernel clusters, in: Proc. of the IEEE Internat. Conf. on Neural Networks and Brain, Vol. 3, 2005, pp. 1885-1890.
28. L. Zeyu, T. Shiwei, X. Jing, J. Jun, Modified FCM clustering based on kernel mapping, in: Proc. of the Internat. Society for Optical Engineering, Vol. 4554, 2001, pp. 241-245.
29. Zhang D.Q., and Chen S.C. Clustering incomplete data using Kernel-based Fuzzy C-Means algorithm. Neural Processing Letters 18 3 (2003) 155-162
30. D. Zhang, S. Chen, Fuzzy clustering using kernel method, in: Proc. of the Internat. Conf. on Control and Automation, 2002, pp. 123-127.
31. L. Zhang, C. Zhou, M. Ma, X. Liu, C. Li, C. Sun, M. Liu, Fuzzy kernel clustering based on particle swarm optimization, in: Proc. of the IEEE Internat. Conf. on Granular Computing, 2006, pp. 428-430.
32. S. Zhou, J. Gan, Mercer kernel fuzzy c-means algorithm and prototypes of clusters, in: Proc. of Conf. on Internat. Data Engineering and Automated Learning, Vol. 3177, 2004, pp. 613-618.