Fuzzy clustering with weighted medoids for relational data

Pattern Recognition

Volumn 43, Issue 5, 2010, Pages 1964-1974

  Mei, Jian Ping ^a   Chen, Lihui ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

(Dis)similarity based; Fuzzy clustering; k Medoids; Prototype weight

Indexed keywords

CLUSTERING APPROACH; DATA ANALYSIS; DATA SETS; EXPERIMENTAL STUDIES; FUZZY CLUSTERS; K-MEDOIDS; MEDOIDS; REAL APPLICATIONS; RELATIONAL DATA; REPRESENTATIVE OBJECT;

FUZZY CLUSTERING; FUZZY SYSTEMS;

CLUSTER ANALYSIS;

EID: 75749127352     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.12.007     Document Type: Article

References (30)

1. Jain A.K., Murty M.N., and Flynn P.J. Data clustering: a review. ACM Computing Surveys 31 3 (1999) 264-323
2. Han J., Kamber M., and Pei J. Data Mining: Concepts and Techniques. second ed. (2005), Morgan Kaufmann, California
3. P. Berkhin, Survey of clustering data mining techniques, Technical Report, Accrue Software, Inc., 2002.
4. Xu R., and Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks 16 3 (2005) 645-678
5. Filippone M., Camastra F., Masulli F., and Rovetta S. A survey of kernel and spectral methods for clustering. Pattern Recognition 41 (2008) 176-190
6. Hathaway R.J., Devenport J.W., and Bezdek J.C. Relational duals of the c-means clustering algorithms. Pattern Recognition 22 (1988) 205-212
7. Sneath P.H.A., and Sokal R.R. Numerical Taxonomy-The Principles and Practice of Numerical Classification (1973), W. H. Freeman, San Francisco
8. Kaufman L., and Rousseeuw P.J. Finding Groups in Data: An Introduction to Cluster Analysis (1990), Wiley, New York
9. Shi J., and Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 (2000) 888-905
10. Schölkopf B., Smola A.J., and Müller K.R. Non-linear component analysis as a kernel eigenvalue problem. Neural Computing 10 (1998) 1299-1319
11. Mitchell T. Machine Learning (1997), McGraw-Hill, New York
12. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum, New York
13. Pal N.R., Pal K., Keller J.M., and Bezdek J.C. A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems 13 (2005) 508-516
14. K. Kummamuru, A. Dhawale, R. Krishnapuram, Fuzzy co-clustering of documents and keywords, in: Proceedings of the 12th IEEE International Conference on Fuzzy Systems, 2003, pp. 772-777.
15. C.-H. Oh, K. Honda, H. Ichihashi, Fuzzy clustering for categorical multivariate data, in: Proceedings of the Joint Ninth IFSA World Congress and 20th NAFIPS International Conference, 2001, pp. 2154-2159.
16. Honda K., Ichihashi H., Masulli F., and Rovetta S. Linear fuzzy clustering with selection of variables using graded possibilistic approach. IEEE Transactions on Fuzzy Systems 15 (2007) 878-889
17. Tjhi W.-C., and Chen L. Dual fuzzy-possibilistic coclustering for categorization of documents. IEEE Transactions on Fuzzy Systems 17 (2009) 532-543
18. Krishnapuram R., Joshi A., Nasraoui O., and Yi L. Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems 9 (2001) 595-607
19. Ng R.T., and Han J. CLARANS: a method for clustering objects for spatial data mining. IEEE Transactions on Knowledge-based Data Engineering 14 (2002) 1003-1016
20. Hathaway R.J., and Bezdek J.C. NERF c-means: non-Euclidean relational fuzzy clustering. Pattern Recognition 27 (1994) 429-437
21. Davé R.N., and Sen S. Robust fuzzy clustering of relational data. IEEE Transactions on Fuzzy Systems 10 (2002) 713-727
22. Bezdek J.C., Hathaway R.J., Huband J.M., Leckie C., and Kotagiri R. Approximate clustering in very large relational data. International Journal of Intelligent Systems 21 (2006) 817-841
23. Wang L., Bezdek J.C., Leckie C., and Kotagiri R. Selective sampling for approximate clustering of very large data sets. International Journal of Intelligence Systems 23 (2008) 313-331
24. Frigui H., Hwang C., and Rhee F.C.-H. Clustering and aggregation of relational data with application to image database categorization. Pattern Recognition 40 (2007) 3053-3068
25. Windham M.P. Numerical classification of proximity data with assignment measures. Journal of Classification 2 (1985) 157-172
26. Bezdek J.C., Hathaway R.J., and Windham M.P. Numerical comparison of the RFCM and AP algorithm for clustering relational data. Pattern Recognition 24 (1991) 783-791
27. S. Miyamoto, K. Umayahara, Fuzzy clustering by quadratic regularization, in: Fuzzy Systems Proceedings, IEEE World Congress on Computational Intelligence, 1998.
28. R.-P. Li, M. Mukaidono, A maximum-entropy approach to fuzzy clustering, in: Proceedings of the Fourth IEEE International Conference on Fuzzy Systems, 1995.
29. N.R. Pal, K. Pal, J.C. Bezdek, A mixed c-means clustering model, in: Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, 1997.
30. University of California at Irving, Machine Learning Repository 〈http://www.ics.uci.edu/∼ mlearn/MLRepository.html〉.