Abstract
In this article, we design an overlapping clustering method in a graph in order to deal with a biological issue: the proteins annotation. Given an unweighted and undirected graph G, we search for subgraphs of G that are dense in edges. The method consists in three steps. First we determine some intial kernels of the classes by means of a local density function; then we improve these kernels using a k-means process; last the kernels are extended to overlapping classes. The method is tested on random graphs and finally applied to a protein interactions network.
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Charon, I., Denoeud, L., Hudry, O. (2007). Overlapping Clustering in a Graph Using k-Means and Application to Protein Interactions Networks. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_16
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DOI: https://doi.org/10.1007/978-3-540-73560-1_16
Publisher Name: Springer, Berlin, Heidelberg
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