Abstract
This paper proposes an approach toward improving re-coloring based clustering with graph b-coloring. Previous b-coloring based clustering algorithm did not consider the quality of clusters. Although a greedy re-coloring algorithm was proposed, it was still restrictive in terms of the explored search space due to its greedy and sequential re-coloring process. We aim at overcoming the limitations by enlarging the search space for re-coloring, while guaranteeing b-coloring properties. A best first re-coloring algorithm is proposed to realize non-greedy search for the admissible colors of vertices. A color exchange algorithm is proposed to remedy the problem in sequential re-coloring. These algorithms are orthogonal with respect to the re-colored vertices and thus can be utilized in conjunction. Preliminary evaluations are conducted over several benchmark datasets, and the results are encouraging.
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References
Baeza, Y., Ribeiro, N.: Modern Information Retrieval (1999)
Bezdek, J., Pal, N.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man and Cybernetics 28(3), 301–315 (1998)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em-algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)
Diestel, R.: Graph Theory. Springer, Heidelberg (2006)
Elghazel, H., Deslandres, V., Hacid, M., Dussauchoy, A., Kheddouci, H.: A new clustering approach for symbolic data and its validation: Application to the healthcare data. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 473–482. Springer, Heidelberg (2006)
Elghazel, H., Yoshida, T., Deslandres, V., Hacid, M., Dussauchoy, A.: A new greedy algorithm for improving b-coloring clustering. In: Proc. of the GbR 2007, pp. 228–239 (2007)
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., Merz, C.: 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., Flynn, P.J.: 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)
von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)
Witten, I., Frank, E.: Weka, http://www.cs.waikato.ac.nz/ml/weka/
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Ogino, H., Yoshida, T. (2010). Toward Improving Re-coloring Based Clustering with Graph b-Coloring. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_21
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DOI: https://doi.org/10.1007/978-3-642-15246-7_21
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