Abstract
This paper proposes a new greedy algorithm to improve the specified b-coloring partition while satisfying b-coloring property. The b-coloring based clustering method in [3] enables to build a fine partition of the data set (classical or symbolic) into clusters even when the number of clusters is not pre-defined. It has several desirable clustering properties: utilization of topological relations between objects, robustness to outliers, all types of data can be accommodated, and identification of each cluster by at least one dominant object. However, it does not consider the high quality of the clusters in the construction of a b-coloring graph. The proposed algorithm in this paper can complement its weakness by re-coloring the objects to improve the quality of the constructed partition under the property and the dominance constraints. The proposed algorithm is evaluated against benchmark datasets and its effectiveness is confirmed.
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References
Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man. and Cybernetics 28(3), 301–315 (1998)
Effantin, B., Kheddouci, H.: The b-chromatic number of some power graphs. Discrete Mathematics and Theoretical Computer Science 6(1), 45–54 (2003)
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: Gerndt, M., Kranzlmüller, D. (eds.) HPCC 2006. LNCS, vol. 4208, pp. 473–482. Springer, Heidelberg (2006)
Guënoche, A., Hansen, P., Jaumard, B.: Efficient algorithms for divisive hierarchical clustering with the diameter criterion. Journal of Classification 8, 5–30 (1991)
Guha, S., Rastogi, R., Shim, K.: Cure: An efficient clustering algorithm for large databases. In: Proceedings of the ACM SIGMOD Conference, pp. 73–84 (1998)
Hansen, P., Delattre, M.: Complete-link cluster analysis by graph coloring. Journal of the American Statistical Association 73, 397–403 (1978)
Hartigan, J., Wong, M.: Algorithm as136: A k-means clustering algorithm. Journal of Applied Statistics 28, 100–108 (1979)
Hettich, S., Blake, C.L., Merz, C.J.: Uci repository of machine learning databases (1998)
Irving, W., Manlov, D.F.: The b-chromatic number of a graph. Discrete Applied Mathematics 91, 127–141 (1999)
Jain, A.K., Murty, M.N.: Data clustering: A review. ACM Computing Surveys 31, 264–323 (1999)
Kalyani, M., Sushmita, M.: Clustering and its validation in a symbolic framework. Pattern Recognition Letters 24(14), 2367–2376 (2003)
Ng, R., Han, J.: Clarans: a method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data. Engineering 14(5), 1003–1016 (2002)
Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers 20, 68–86 (1971)
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Elghazel, H., Yoshida, T., Deslandres, V., Hacid, MS., Dussauchoy, A. (2007). A New Greedy Algorithm for Improving b-Coloring Clustering. In: Escolano, F., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2007. Lecture Notes in Computer Science, vol 4538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72903-7_21
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DOI: https://doi.org/10.1007/978-3-540-72903-7_21
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