Abstract
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.
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Elghazel, H., Kheddouci, H., Deslandres, V., Dussauchoy, A. (2007). A Partially Dynamic Clustering Algorithm for Data Insertion and Removal. In: Corruble, V., Takeda, M., Suzuki, E. (eds) Discovery Science. DS 2007. Lecture Notes in Computer Science(), vol 4755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75488-6_9
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DOI: https://doi.org/10.1007/978-3-540-75488-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75487-9
Online ISBN: 978-3-540-75488-6
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