Kernel-based hard clustering methods in the feature space with automatic variable weighting

Pattern Recognition

Volumn 47, Issue 9, 2014, Pages 3082-3095

  Ferreira, Marcelo R P ^a   De Carvalho, Francisco De A T ^b  

^a FEDERAL UNIVERSITY OF PARAÍBA   (Brazil)

^b FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

Adaptive distances; Clustering analysis; Feature space; Kernel clustering

Indexed keywords

TOOLS;

ADAPTIVE DISTANCE; BENCHMARK DATASETS; CLUSTERING ANALYSIS; DISSIMILARITY MEASURES; FEATURE SPACE; HARD CLUSTERING ALGORITHMS; INTERPRETATION TOOLS; KERNEL CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 84900833214     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.03.026     Document Type: Article

References (49)

1. R.C. de Amorim, and B. Mirkin Minkowski metric, feature weighting and anomalous cluster initializing in k-means clustering Pattern Recognit. 45 2012 1061 1075
2. R.A. Baeza-Yates, and B. Ribeiro-Neto Modern Information Retrieval 1999 Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA
3. J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Plenum New York
4. L. Breiman, J. Friedman, C.J. Stone, and R.A. Olshen Classification and Regression Trees 1984 Chapman & Hall/CRC Boca Raton
5. F. Camastra, and A. Verri A novel kernel method for clustering IEEE Trans. Neural Netw. 27 2005 801 804
6. B. Caputo, K. Sim, F. Furesjo, A. Smola, Appearance-based object recognition using SVMS: which kernel should I use?, in: Proceedings of NIPS Workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision
7. G. Celeux, E. Diday, G. Govaert, Y. Lechevallier, and H. Ralambondrainy Classification Automatique des Données 1989 Bordas Paris
8. C.C. Chang, and C.J. Lin Libsvm a library for support vector machines ACM Trans. Intell. Syst. Technol. 2 2011 27:1 27:27
9. M. Chavent, F.D. Carvalho, Y. Lechevallier, and R. Verde New clustering methods for interval data Comput. Stat. 21 2006 211 229
10. S.C. Chen, and D.Q. Zhang Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure IEEE Trans. Syst. Man Cybern. 34 2004 1907 1916
11. J.H. Chiang, and P.Y. Hao A new kernel-based fuzzy clustering approach support vector clustering with cell growing IEEE Trans. Fuzzy Syst. 11 2003 518 527
12. F.A.T. De Carvalho, C.P. Tenório, and N.L. Cavalcanti Jr. Partitional fuzzy clustering methods based on adaptive quadratic distances Fuzzy Sets Syst. 157 2006 2833 2857
13. I.S. Dhillon, Y. Guan, B. Kulis, Kernel k-means spectral clustering and normalized cuts, in: Proceedings 10th ACM International Conference on Knowledge Discovery and Data Mining, pp. 551-556
14. M. Filippone, F. Camastra, F. Masulli, and S. Rovetta A survey of kernel and spectral methods for clustering Pattern Recognit. 41 2008 176 190
15. A. Frank, A. Asuncion, UCI Machine Learning Repository, 2010
16. M. Girolami Mercer kernel-based clustering in feature space IEEE Trans. Neural Netw. 13 2002 780 784
17. A.D. Gordon Classification 2nd edition 1999 Chapman & Hall Boca Raton
18. T. Graepel, K. Obermayer, Fuzzy topographic kernel clustering, in: W. Brauer (Ed.), Proceedings of the Fifth GI Workshop Fuzzy Neuro Systems 98, pp. 90-97
19. D. Graves, and W. Pedrycz Kernel-based fuzzy clustering and fuzzy clustering a comparative experimental study Fuzzy Sets Syst. 161 2010 522 543
20. A.S. Have, M.A. Girolami, and J. Larsen Clustering via kernel decomposition IEEE Trans. Neural Netw. 17 2006 256 264
21. J.Z. Huang, M.K. Ng, H. Rong, and Z. Li Automated variable weighting in k-means type clustering IEEE Trans. Pattern Anal. Mach. Intell. 27 2005 657 668
22. L. Hubert, and P. Arabie Comparing partitions J. Class. 2 1985 193 218
23. R. Inokuchi, S. Miyamoto, LVQ clustering and SOM using a kernel function, in: Proceedings of IEEE International Conference on Fuzzy Systems, vol. 3, pp. 1497-1500
24. A.K. Jain, and R.C. Dubes Algorithms for Clustering Data 1988 Prentice Hall Englewood Cliffs, New Jersey
25. A.K. Jain, M.N. Murty, and P.J. Flynn Data clustering a review ACM Comput. Surv. 31 1999 233 264
26. L. Jing, M.K. Ng, and J.Z. Huang An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data IEEE Trans. Knowl. Data Eng. 19 2007 1026 1041
27. D.W. Kim, K.Y. Lee, D. Lee, and K.H. Lee Evaluation of the performance of clustering algorithms in kernel-induced feature space Pattern Recognit. 38 2005 607 611
28. D.W. Kim, K.Y. Lee, D. Lee, and K.H. Lee A kernel-based subtractive clustering method Pattern Recognit. Lett. 26 2005 879 891
29. T. Kohonen Self-organized formation of topologically correct feature maps Biol. Cybern. 43 1982 59 69
30. T. Kohonen, The self-organizing map, in: Proceedings of the IEEE, vol. 78, pp. 1464-1480
31. Y. Lu, S. Wang, S. Li, and C. Zhou Particle swarm optimizer for variable weighting in clustering high-dimensional data Mach. Learn. 82 2011 43 70
32. D. Macdonald, C. Fyfe, The kernel self-organizing map, in: Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 317-320
33. T.M. Martinetz, S.G. Berkovich, and K.J. Schulten 'Neural gas' network for vector quantization and its application to time-series prediction IEEE Trans. Neural Netw. 4 1993 558 569
34. J. Mercer, Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations, Philosophical Transactions of the Royal Society of London, Series A, Containing Papers of a Mathematical or Physical Character, Vol. 209 (1909), pp. 415-446
35. G.W. Milligan Clustering and Classification 1996 World Scientific Singapore pp. 341-375
36. B. Mirkin Mathematical Classification and Clustering 1996 Kluwer Academic Publishers Dordrecht, Boston, London
37. K.R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf An introduction to kernel-based learning algorithms IEEE Trans. Neural Netw. 12 2001 181 202
38. C.H. Nguyen, and T.B. Ho An efficient kernel matrix evaluation measure Pattern Recognit. 41 2008 3366 3372
39. A.K. Qinand, P.N. Suganthan, Kernel neural gas algorithms with application to cluster analysis, in: ICPR - 17th International Conference on Pattern Recognition (ICPR04), vol. 4, pp. 617-620
40. B. Scholkopf, and A.J. Smola Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2002 The MIT Press London
41. B. Scholkopf, A.J. Smola, and K.R. Müller Nonlinear component analysis as a kernel eigenvalue problem Neural Comput. 10 1998 1299 1319
42. H. Shen, J. Yang, S. Wang, and X. Liu Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets Soft Comput. 10 2006 1061 1073
43. C.Y. Tsai, and C.C. Chiu Developing a feature weight self-adjustment mechanism for a k-means clustering algorithm Comput. Stat. Data Anal. 52 2008 4658 4672
44. D.M. Tsai, and C.C. Lin Fuzzy c-means based clustering for linearly and nonlinearly separable data Pattern Recognit. 44 2011 1750 1760
45. J. Wang, H. Lu, K.N. Plataniotis, and J. Lu Gaussian kernel optimization for pattern classification Pattern Recognit. 42 2009 1237 1247
46. R.R. Yager, and D.P. Filev Approximate clustering via the mountain method IEEE Trans. Systems, Man, Cybernet. 24 1994 1279 1284
47. X. Yin, S. Chen, E. Hu, and D. Zhang Semi-supervised clustering with metric learning an adaptive kernel method Pattern Recognit. 43 2010 1320 1333
48. J. Yu, Q. Cheng, and H. Huang Analysis of the weighting exponent in the fcm IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34 2004 634 639
49. D.Q. Zhang, S.C. Chen, Fuzzy clustering using kernel method, in: The 2002 International Conference on Control and Automation, 2002 ICCA, pp. 162-163.