Uncertainty modeling in dynamic clustering - A soft computing perspective

2010 IEEE World Congress on Computational Intelligence, WCCI 2010

Volumn , Issue , 2010, Pages

  Peters, Georg ^a   Weber, Richard ^b   Crespo, Fernando ^c  

^a MUNICH UNIVERSITY OF APPLIED SCIENCES   (Germany)

^b UNIVERSITY OF CHILE   (Chile)

^c UNIVERSIDAD DE VALPARAÍSO   (Chile)

Author keywords

Dynamic data mining; K means clustering; Uncertainty modeling

Indexed keywords

CLASS ASSIGNMENTS; CLUSTER ALGORITHMS; CUSTOMER SEGMENTATION; DYNAMIC CLUSTERING; DYNAMIC DATA MINING; DYNAMIC ENVIRONMENTS; K-MEANS CLUSTERING; PROBABILITY THEORY; RESEARCH ISSUES; ROUGH SET; UNCERTAINTY MODELING;

ARTIFICIAL INTELLIGENCE; DATA MINING; FUZZY LOGIC; FUZZY SET THEORY; PROBABILISTIC LOGICS; SOFT COMPUTING;

CLUSTER COMPUTING;

EID: 78549264817     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2010.5584840     Document Type: Conference Paper

References (26)

1. P. Arabie and L. Hubert, Advanced Methods in Marketing Research. Oxford: Blackwell, 1994, ch. Cluster Analysis in Marketing Research, pp. 160-189.
2. R. Weber, Encyclopedia of Data Warehousing and Mining, 2nd ed. Hershey, USA: IGI Global, 2009, ch. Dynamic Data Mining, pp. 722-728.
3. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht, Netherlands: Kluwer Academic Publishers, 1991.
4. Z. Pawlak and A. Skowron, "Rudiments of rough sets," Information Sciences, vol. 177, no. 1, pp. 3-27, 2007.
5. R. Nowicki, "Rough neuro-fuzzy structures for classification with missing data," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 39, no. 6, pp. 1334-1347, 2009.
6. P. Maji and S. K. Pal, "Fuzzy-rough sets for information measures and selection of relevant genes from microarray data," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. tdb, no. tbd, p. tbd, 2010.
7. E. Diday, "The dynamic cluster method in non- hierarchical clustering," International Journal of Parallel Programming, vol. 2, pp. 61-88, 1973.
8. G. Karypis, E. Han, and V. Kumar, "Chameleon: A hierarchical clustering algorithm using dynamic modeling," IEEE Computer, Special Issue on Data Analysis and Mining, vol. 32, no. 8, pp. 68-75, 1999.
9. J. Bezdek and N. Pal, "Some new indexes of cluster validity," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 28, pp. 301-315, 1998.
10. O. Georgieva and F. Klawonn, "Dynamic data assigning assessment clustering of streaming data," Applied Soft Computing, vol. 8, no. 4, pp. 1305-1313, 2008.
11. J. Beringer and E. Huellermeier, Advances in Fuzzy Clustering and Its Applications. Hoboken, New Jersey, USA: John Wiley and Sons, 2007, ch. Fuzzy Clustering of Parallel Data Streams, pp. 333-352.
12. P. Lingras, M. Hogo, and M. Snorek, "Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets," Web Intelligence and Agent Systems, vol. 2, no. 3, pp. 217-225, 2004. (Pubitemid 40963314)
13. P. Lingras, M. Hogo, M. Snorek, and C. West, "Temporal analysis of clusters of supermarket customers: conventional versus interval set approach," Information Sciences, vol. 172, no. 1-2, pp. 215-240, 2005.
14. D. Davies and D. Bouldin, "A cluster separation measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, pp. 224-227, 1979.
15. M. Windham, "Cluster validity for fuzzy clustering algorithms," Fuzzy Sets and Systems, vol. 5, pp. 177-185, 1981.
16. J. Bezdek, Pattern Recognition with Fuzzy Objective Algorithms. New York: Plenum Press, 1981.
17. S. Ranka, S. Aluru, R. Buyya, Y. C. Chung, S. Dua, A. Grama, S. K. S. Gupta, K. R., and V. V. Phoha, "FCM for gene expression bioinformatics data," in Contemporary Computing (IC3 2009), ser. Communications in Computer and Information Science. Springer Verlag, 2009, vol. 40, pp. 521-532.
18. J. S. Ahna and S. Y. So Young Sohn, "Customer pattern search for after-sales service in manufacturing," Expert Systems with Applications, vol. 36, no. 3, pp. 5371-5375, 2009.
19. P. Lingras and C. West, "Interval set clustering of web users with rough k-means," Department of Mathematics and Computer Science, St. Mary's University, Halifax, Canada, Tech. Rep. 2002-002, 2002.
20. --, "Interval set clustering of web users with rough k-means," Journal of Intelligent Information Systems, vol. 23, pp. 5-16, 2004.
21. P. Lingras, "Applications of rough set based k-means, Kohonen SOM, GA clustering," Transactions on Rough Sets, vol. VII, pp. 120-139, 2007.
22. S. Mitra, "An evolutionary rough partitive clustering," Pattern Recognition Letters, vol. 25, pp. 1439-1449, 2004.
23. G. Peters, "Some refinements of rough k-means," Pattern Recognition, vol. 39, pp. 1481-1491, 2006.
24. G. Peters, M. Lampart, and R. Weber, "Evolutionary rough k-medoids clustering," Transactions on Rough Sets, vol. VIII, pp. 289-306, 2008.
25. F. Crespo and R. Weber, "A methodology for dynamic data mining based on fuzzy clustering," Fuzzy Sets and Systems, vol. 150, pp. 267-284, 2005.
26. G. Peters and R. Weber, "Intelligent cluster algorithms for changing data structures," International Journal of Intelligent Defense Systems, vol. 2, no. 2, pp. 105-119, 2009.