Online fuzzy medoid based clustering algorithms

Neurocomputing

Volumn 126, Issue , 2014, Pages 141-150

  Labroche, Nicolas ^a  

^a SORBONNE UNIVERSITÉ   (France)

Author keywords

Fuzzy clustering; Large dataset; Medoids; Online; Stream

Indexed keywords

COMPUTATION TIME; DECAY MECHANISMS; INTERPRETABILITY; LARGE DATASET; MEDOIDS; ONLINE; OVERLAPPING CLUSTERS; STREAM;

DATA COMMUNICATION SYSTEMS; EXPERIMENTS; FUZZY CLUSTERING; ISOMERS;

CLUSTERING ALGORITHMS;

ARTICLE; CLUSTER ANALYSIS; DATA PROCESSING; FUZZY SYSTEM; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; ONLINE ANALYSIS; ONLINE SYSTEM; PRIORITY JOURNAL; VALIDATION STUDY;

EID: 84887610180     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.07.057     Document Type: Article

References (47)

1. Angelov P. Fuzzily connected multimodel systems evolving autonomously from data streams. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2011, 41(4):898-910.
2. Bezdek J. Pattern Recognition with Fuzzy Objective Function Algorithms 1981, Plenum Press, New York.
3. Guha S., Meyerson A., Mishra N., Motwani R., O'Callaghan L. Clustering data streams. theory and practice. IEEE Trans. Knowl. Data Eng. 2003, 15(May-June (3)):515-528.
4. P. Hore, L. Hall, D. Goldgof, W. Cheng, Online fuzzy c means, in: Fuzzy Information Processing Society, NAFIPS 2008, 2008, pp. 1-5.
5. N. Labroche, New incremental fuzzy c medoids clustering algorithms, in: Proceedings of the North American Fuzzy Information Processing Society 2010, Toronto, Canada, 2010, pp. 145-150.
6. Wu X., Kumar V., Quinlan J.R., Ghosh J., Yang Q., Motoda H., McLachlan G., Ng A., Liu B., Yu P., Zhou Z.-H., Steinbach M., Hand D., Steinberg D. Top 10 algorithms in data mining. Knowl. Inf. Syst. 2008, 14(1):1-37.
7. J.B. MacQueen, Some methods for classification and analysis of multivariate observations, in: University of California Press (Ed.), Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, Berkeley, 1967, pp. 281-297.
8. P.H.A. Sneath, R.R. Sokal, Numerical Taxonomy-The Principles and Practice of Numerical Classification, Technical Report, W. H. Freeman, San Francisco, 1973.
9. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large databases, in: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada, 1996, pp. 103-114.
10. Y. Fu, K. Sandhu, M. Shih, A generalization-based approach to clustering of web usage sessions, in: Masand, Spiliopoulou (Eds.), Web Usage Analysis and User Profiling, vol. 1836 of LNCS, Springer, 2000, pp. 21-38.
11. M. Hasan, V. Chaoji, S. Salem, M. Zaki, Robust partitional clustering by outlier and density insensitive seeding, Pattern Recognition Lett. 30 (11) (2009) 994-1002.
12. Hochbaum D.S., Shmoys D.B. A best possible heuristic for the k-center problem. Math. Oper. Res. 1985, 10(2):180-184.
13. A.M. Bensaid, L.O. Hall, J.C. Bezdek, L.P. Clarke, Partially supervised clustering for image segmentation, Pattern Recognition 29 (5) (1996).
14. Pedrycz W. Algorithm of fuzzy clustering with partial supervision. Pattern Recognition Lett. 1985, 3:13-20.
15. Pedrycz W., Waletzky J. Fuzzy clustering with partial supervision. Man Cybern. 1997, 27(5):787-795.
16. J. Heer, E. Chi, Identification of web user traffic composition using multi-modal clustering and information scent, in: Proceedings of the Workshop on Web Mining, SIAM Conference on Data Mining, 2001, pp. 51-58.
17. Hathaway R., Bezdek J., Davenport J. On relational data versions of c-means algorithms. Pattern Recognition Lett. 1996, 17:607-612.
18. Hathaway R., Davenport J., Bezdek J. Relational dual of the c-means clustering algorithms. Pattern Recognition 1989, 22(2):205-212.
19. Hathaway R., Bezdek J. Nerf c-means. non-Euclidian relational fuzzy clustering. Pattern Recognition 1994, 27:429-437.
20. J.C. Bezdek, R.J. Hathaway, J.M. Huband, C. Leckie, R.Kotagiri, Approximate clustering in very large relational data, Int. J. Intell. Syst. 21 (8) (2006).
21. L. Kaufman, P. Rousseeuw, Clustering by means of medoids, in: Statistical Data Analysis based on the L1 Norm, Elsevier, 1987, pp. 405-416.
22. L. Kaufman, P. Rousseeuw, in: Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley and Sons, 2005.
23. M.C.N. Barioni, H.L. Razente, A.J.M. Traina, C. Traina Jr., An efficient approach to scale up k-medoid based algorithms in large databases, in: Proceedings of the XXI Simpósio Brasileiro de Banco de Dados, 2006.
24. V. Estivill-Castro, J. Yang, Categorizing visitors dynamically by fast and robust clustering of access logs, in: Lecture Notes in Computer Science, Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development, 2001, pp. 498-507.
25. Krishnapuram R., Joshi A., Nasraoui O., Yi L. Low-complexity fuzzy relational clustering algorithms for web mining. IEEE-FS 2001, 9:595-607.
26. Nasraoui O., Krishnapuram R., Joshi A., Kamdar T. Automatic web user profiling and personalization using robust fuzzy relational clustering. E-Commerce and Intelligent Methods 2002, Springer. J. Kacprzyk (Ed.).
27. P. Angelov, D. Filev, Simpl_ets: a simplified method for learning evolving Takagi-Sugeno fuzzy models, in: International Conference on Fuzzy Systems, 2005, pp. 1068-1072.
28. Kasabov N., Song Q. Denfis. dynamic evolution neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 2002, 10(2):144-154.
29. Juang C., Lin C. An on-line self constructing neural fuzzy inference network. IEEE Trans. Neural Networks 1998, 6(1):12-32.
30. Rubio J.J., Vàsquez D.M., Pacheco J. Backpropagation to train an evolving radial basis function neural network. Evolving Syst. 2010, 1:173-180.
31. Rong H., Sundarajan N., Huang G., Saratchandran P. Sequential adaptive fuzzy inference system for non linear system identification and prediction. Fuzzy Sets Syst. 2006, 157(9):1260-1275.
32. Leng G., Ginnity T.M., Prasad G. An approach for online extraction of fuzzy rules using a self-organizing fuzzy neural networks. Fuzzy Sets Syst. 2005, 150:211-243.
33. M.M. Gaber, A. Zaslavsky, S. Krishnaswamy, Mining data streams: a review, ACM SIGMOD Rec. 34 (2) (2005).
34. A. Laurent, M.-J. Lesot (Eds.), Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, IGI Global, 2009.
35. C.C. Aggarwal, T.J. Watson, R. Ctr, J. Han, J. Wang, P.S. Yu, A framework for clustering evolving data streams, in: In VLDB, 2003, pp. 81-92.
36. Philipp Kranen C.B., Assent Ira, Seidl T. The clustree. indexing micro-clusters for anytime stream mining. Knowl. Inf. Syst. 2011, 29(2):249-272.
37. C. Aggarwal, J. Han, J. Wang, P. Yu, A framework for projected clustering of high dimensional data streams, in: Proceedings of the 30th International Conference on Very Large Databases, 2004, pp. 852-863.
38. W. Liu, J. O. Yang, Clustering algorithm for high dimensional data stream over sliding windows, in: International Joint COnference of IEEE TrustCom-11, 2011, pp. 1537-1542.
39. F. Cao, M. Ester, W. Qian, A.Zhou, Density-based clustering over an evolving data stream with noise, in: In 2006 SIAM Conference on Data Mining, 2006, pp. 328-339.
40. Y. Chen, L. Tu (Eds.), D.-B.C. for Real-Time Stream Data, Proceedings of KDD'07 Conference. ACM, 2007.
41. C. Ruiz, E. Menasalvas, M. Spiliopoulou (Eds.), C-denstream: Using Domain Knowledge on a Data Stream. S.-V.B. Heidelberg, DS 2009, LNAI 5808, 2009.
42. N. Haga, K. Honda, H. Ichihashi, A. Notsu, Linear fuzzy clustering of relational data based on extended fuzzy c-medoids, in: IEEE International Conference on Fuzzy Systems, 2008. FUZZ-IEEE 2008, Hong Kong, 2008, pp. 1098-7584.
43. M.M.-T. Chiang, B. Mirkin, Intelligent choice of the number of clusters in k-means clustering: an experimental study with different cluster spreads, J. Classification 27 (2010).
44. P. Kranen, H. Kremer, T. Jansen, T. Seidl, A. Bifet, G. Holmes, B. Pfahringer, Clustering performance on evolving data streams: assessing algorithms and evaluation measures within moa, in: Data Mining Workshops (ICDMW), 2010 IEEE International Conference on Data Mining Workshops, 2010, pp. 1400-1403.
45. H. Kremer, P. Kranen, T. Jansen, T. Seidl, A. Bifet, G. Holmes, B. Pfahringer, An effective evaluation measure for clustering on evolving data streams, in: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, ACM, New York, NY, USA, 2011, pp. 868-876.
46. U.M.L. Repository, 〈〉 (2010). http://archive.ics.uci.edu/ml/.
47. J. Wu, H. Xiong, J. Chen, Adapting the right measures for k-means clustering, in: Proceedings of the KDD 2009 Conference, ACM, 2009, pp. 877-885.