The b-chromatic number of a graph
Discrete Applied Mathematics
ACM Computing Surveys (CSUR)
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
A new clustering approach for symbolic data and its validation: application to the healthcare data
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Stream Clustering of Growing Objects
DS '09 Proceedings of the 12th International Conference on Discovery Science
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We consider the problem of dynamic clustering which has been addressed in many contexts and applications including dynamic information retrieval, Web documents classification, etc. The goal is to efficiently maintain homogenous and well-separated clusters as new data are inserted or existing data are removed. We propose a framework called dynamic b-coloring clustering based solely on pairwise dissimilarities among all pairs of data and on cluster dominance. In experiments on benchmark data sets, we show improvements in the performance of clustering solution in terms of quality and computational complexity.