An extension to rough c-means clustering algorithm based on boundary area elements discrimination

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7736 LNCS, Issue , 2013, Pages 17-33

  Li, Fan ^a   Liu, Qihe ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Boundary area; Lower approximation; Rough c means algorithm; Rough Sets; Upper approximation

Indexed keywords

APPROXIMATION ALGORITHMS; CLUSTER COMPUTING; MACHINE LEARNING; ROUGH SET THEORY;

BOUNDARY AREA; C-MEANS ALGORITHMS; C-MEANS CLUSTERING ALGORITHM; CLUSTERING RESULTS; DATA POINTS; LOWER APPROXIMATION; UCI MACHINE LEARNING REPOSITORY; UPPER APPROXIMATION;

CLUSTERING ALGORITHMS;

EID: 84873338633     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-36505-8_2     Document Type: Conference Paper

References (44)

1. Mirkin, B.: Mathematical Classification and Clustering. Kluwer Academic Publishers, Dordrecht (1996
2. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Inc., New York (2001
3. Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, London (1974
4. Lloyd, S.P.: Least squares quantization in pcm. IEEE Trans. Information Theory, 129-137 (1982
5. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981
6. Cannon, R., Dave, J., Bezdek, J.: Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(2), 248-255 (1986 (Pubitemid 16595427)
7. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems 14, pp. 849-856. MIT Press (2001
8. Yu, S., Shi, J.: Multiclass spectral clustering. In: Proceedings of the 9th IEEE International Conference on Computer Vision, pp. 313-319. IEEE Computer Society (2003
9. Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988
10. Jain, A., Murty, M., Flynn, P.: Data clustering: A review. ACM Computing Surveys (CSUR) 31(3), 264-323 (1999
11. Smyth, P.: Clustering sequences with hidden markov models. Advances in Neural Information Processing Systems, 648-654 (1997
12. Rose, K.: Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proceedings of the IEEE 86(11), 2210-2239 (1998 (Pubitemid 128720301)
13. Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Boston (1991
14. Pawlak, Z.: Some Issues on Rough Sets. In: Peters, J.F., Skowron, A., Grzyma-la-Busse, J.W., Kostek, B., Swiniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 1-58. Springer, Heidelberg (2004
15. Lingras, P., West, C.: Interval set clustering of web users with rough k-means. Journal of Intelligent Information Systems 23(1), 5-16 (2004
16. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Information Sciences 177(1), 41-73 (2007 (Pubitemid 44556700)
17. Bazan, J.G., Skowron, A.: Dynamic Reducts as a Tool for Extracting Laws from Decision Tables. In: Ra's, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 346-355. Springer, Heidelberg (1994
18. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning 15(4), 333-365 (1996 (Pubitemid 126414910)
19. Polkowski, L.: A set theory for rough sets: Toward a formal calculus of vague. Fundamenta Informaticae 71(1), 49-61 (2006 (Pubitemid 43848919)
20. Peters, J.F., Szczuka, M.S.: Rough Neurocomputing: A Survey of Basic Models of Neurocomputation. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 308-315. Springer, Heidelberg (2002
21. Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. IEEE Transactions on Knowledge and Data Engineering 12(2), 331-336 (2000
22. Yao, Y.Y.: Constructive and algebraic methods of theory of rough sets. Information Sciences 109, 21-47 (1998
23. Yao, Y.Y.: Decision-Theoretic Rough Set Models. In: Yao, J., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Sļezak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 1-12. Springer, Heidelberg (2007
24. Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Information Sciences 180(3), 341-353 (2010
25. Pal, S., Mitra, P.: Case generation using rough sets with fuzzy representation. IEEE Transactions on Knowledge and Data Engineering 16(3), 292-300 (2004
26. Zhu,W., Wang, F.: Reduction and axiomization of covering generalized rough sets. Information Sciences 152(1), 217-230 (2003
27. Zhu, W., Wang, F.: On three types of covering rough sets. IEEE Transactions on Knowledge and Data Engineering 19(8), 1131-1144 (2007
28. Min, F., He, H.P., Qian, Y.H., Zhu, W.: Test-cost-sensitive attribute reduction. Information Sciences 181, 4928-4942 (2011
29. Min, F., Zhu, W.: Attribute reduction of data with error ranges and test costs. Information Sciences 211, 48-67 (2012
30. Mitra, S.: An evolutionary rough partitive clustering. Pattern Recognition Letters 25(12), 1439-1449 (2004
31. Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(4), 795-805 (2006 (Pubitemid 44178150)
32. Mitra, S., Pedrycz, W., Barman, B.: Shadowed c-means: Integrating fuzzy and rough clustering. Pattern Recognition 43(4), 1282-1291 (2010
33. Peters, G.: Some refinements of rough k-means clustering. Pattern Recognition 39(8), 1481-1491 (2006
34. Peters, G., Lampart, M., Weber, R.: Evolutionary Rough k-Medoid Clustering. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 289-306. Springer, Heidelberg (2008
35. Peters, G., Weber, R., Nowatzke, R.: Dynamic rough clustering and its applications. Applied Soft Computing, 3193-3207 (2012
36. Li, F., Liu, Q.: An Extension to Rough c-Means Clustering. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 208-216. Springer, Heidelberg (2011
37. Lingras, P.: Applications of Rough Set Based K-Means, Kohonen SOM, GA Clustering. In: Peters, J.F., Skowron, A., Marek, V.W., Or-lowska, E., S-lowi'nski, R., Ziarko, W.P. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 120-139. Springer, Heidelberg (2007
38. Hassanien, A., Abraham, A., Peters, J., Schaefer, G., Henry, C.: Rough sets and near sets in medical imaging: A review. IEEE Transactions on Information Technology in Biomedicine 13, 955-968 (2009
39. Mitra, S., Barman, B.: Rough-Fuzzy Clustering: An Application to Medical Imagery. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 300-307. Springer, Heidelberg (2008
40. Wang, G.: Rough Set Theory and Knowledge Acquisition. Xi'an Jiaotong University Press (2001) (in Chinese
41. Bottou, L., Vapnik, V.: Local learning algorithms. Neural Computation 4(6), 888-900 (1992
42. Wu, M., Schölkopf, B.: A local learning approach for clustering. Advances in Neural Information Processing Systems 19, 1529-1536 (2006
43. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/mlrepository.html
44. Bezdek, J., Pal, N.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 28(3), 301-315 (1998 (Pubitemid 128752585)