A graph b-coloring framework for data clustering

Journal of Mathematical Modelling and Algorithms

Volumn 7, Issue 4, 2008, Pages 389-423

  Elghazel, Haytham ^a,b   Kheddouci, Hamamache ^a,b   Deslandres, Véronique ^a,b   Dussauchoy, Alain ^a,b  

^a UNIVERSITÉ DE LYON   (France)

^b UNIVERSITÉ LYON 1   (France)

Author keywords

Cluster validity indices; Clustering; Graph b coloring

Indexed keywords

EID: 57849115416     PISSN: 15701166     EISSN: 15729214     Source Type: Journal    
DOI: 10.1007/s10852-008-9093-x     Document Type: Article

References (33)

1. M. Ichino H. Yaguchi 1994 General minkowsky metrics for mixed feature type data analysis IEEE Trans. Syst. Man Cybern. 24 698 708
2. Malerba, D., Esposito, F., Gioviale, V., Tamma, V.: Comparing dissimilarity measures in symbolic data analysis. In: Joint Conferences on New Techniques and Technologies for Statistics and Exchange of Technology and Know-how, pp. 473-481, Hersonissos, 18-22 June 2001
3. A.K. Jain M.N. Murty T.P.J. Flynn 1999 Data clustering: a review ACM Comput. Surv. 31 264 323
4. Guha, S., Rastogi, R., Shim, K.: Cure: an efficient clustering algorithm for large databases. In: Proceedings of the ACM SIGMOD Conference, pp. 73-84, Seattle, 2-4 June 1998
5. G. Karypis E. Han K.V. Chameleon 1999 A hierarchical clustering algorithm using dynamic modeling IEEE Comput. 32 8 68 75
6. R. Ng J. Han 2002 Clarans: a method for clustering objects for spatial data mining IEEE Trans. Knowl. Data Eng. 14 5 1003 1016
7. J. Hartigan M. Wong 1979 Algorithm as136: a k-means clustering algorithm J. Appl. Stat. 28 100 108
8. J.G. Auguston J. Minker 1970 An analysis of some graph theoretical clustering techniques J. Assoc. Comput. Mach. 17 4 571 588
9. C.T. Zahn 1971 Graph-theoretical methods for detecting and describing gestalt clusters IEEE Trans. Comput. 20 68 86
10. Matula, D.W.: Cluster analysis via graph theoretic techniques. In: Mullin, R.C., Reid, K.B., Roselle, D.P. (eds.) The Louisiana Conference on Combinatorics, Graph Theory, and Computing, pp. 199-212. Louisiana State University, Baton Rouge (1970)
11. D.W. Matula 1972 K-components, clusters, and slicings in graphs SIAM J. Appl. Math. 22 459 480
12. J. Shi J. Malik 2000 Normalized cuts and image segmentation IEEE Trans. Pattern Anal. Mach. Intell. 22 8 888 905
13. Z. Wu R. Leahy 1993 An optimal graph theoretic approach to data clustering: theory and its application to image segmentation IEEE Trans. Pattern Anal. Mach. Intell. 15 11 1101 1113
14. C.C. Gotlieb S. Kumar 1968 Semantic clustering of index terms J. Assoc. Comput. Mach. 15 4 493 513
15. A. Guenoche P. Hansen B. Jaumard 1991 Efficient algorithms for divisive hierarchical clustering with the diameter criterion J. Classif. 8 5 30
16. E. Hartuv R. Shamir 2000 A clustering algorithm based on graph connectivity Inf. Process. Lett. 76 175 181
17. R.E. Gomory T.C. Hu 1961 Multy-terminal network flows SIAM J. Appl. Math. 9 551 560
18. Kuhns, J.L.: Mathematical analysis of correlation clusters. Word correlation and automatic indexing, 2 (1959)
19. P. Hansen M. Delattre 1978 Complete-link cluster analysis by graph coloring J. Am. Stat. Assoc. 73 397 403
20. Elghazel, H., Kheddouci, H., Deslandres, V., Dussauchoy, A.: A partially dynamic clustering algorithm for data insertion and removal. In: Corruble, V., Takeda, M., Suzuki, E. (eds.) DS2007 (Springer Verlag LNAI 4755), pp. 78-90. Springer, New York (2007)
21. W. Irving D.F. Manlov 1999 The b-chromatic number of a graph Discrete Appl. Math. 91 127 141
22. B. Effantin H. Kheddouci 2003 The b-chromatic number of some power graphs Discret. Math. Theor. Comput. Sci. 6 1 45 54
23. M. Kouider M. Maheo 2002 Some bounds for the b-chromatic number of a graph Discret. Math. 256 267 277
24. Effantin, B., Kheddouci, H.: A distributed algorithm for a b-coloring of a graph. In: ISPA2006 (Springer Verlag LNCS 4330), pp. 430-438. Springer, New York (2006)
25. Kheddouci, H.: Placement et paramètres de graphes. Mémoire HDR RC 20586, University of Bourgogne (2003)
26. Elghazel, H., Deslandres, V., Hacid, M.S., Dussauchoy, A., Kheddouci, H.: A new clustering approach for symbolic data and its validation: application to the healthcare data. In: Esposito, F., Ras, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS2006 (Springer Verlag LNAI 4203), pp. 473-482. Springer, New York (2006)
27. J.C. Bezdek N.R. Pal 1998 Some new indexes of cluster validity IEEE Trans. Syst. Man Cybern. 28 3 301 315
28. M. Kalyani M. Sushmita 2003 Clustering and its validation in a symbolic framework Pattern Recogn. Lett. 24 14 2367 2376
29. Blake, C.L., Merz, C.J.: Uci Repository of Machine Learning Databases. University of California Irvine, Irvine (1998)
30. S. Guha R. Rastogi K. Shim A. Rock 2000 robust clustering algorithm for categorical attributes Inf. Syst. 25 5 345 366
31. G. Biswas J.B. Weinberg D.H. Fisher 1998 Iterate: a conceptual clustering algorithm for data mining IEEE Trans. Syst. Man Cybern. 28 C 219 230
32. W.M. Rand 1971 Objective criteria for the evaluation of clustering methods J. Am. Stat. Assoc. 66 846 850
33. G. Rote 1991 Computing the minimum Hausdorff distance between two point sets on a line under translation Inf. Process. Lett. 38 123 127