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Self-Organizing Maps for clustering and visualization of bipartite graphs

Abstract : Graphs (also frequently called networks) have attracted a burst of attention in the last years, with applications to social science, biology, computer science... The present paper proposes a data mining method for visualizing and clustering the nodes of a peculiar class of graphs: bipartite graphs. The method is based on a self-organizing map algorithm and relies on an extension of this approach to data described by a dissimilarity matrix.
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https://hal.archives-ouvertes.fr/hal-01001991
Contributor : Nathalie Vialaneix <>
Submitted on : Thursday, June 5, 2014 - 1:19:41 PM
Last modification on : Tuesday, January 19, 2021 - 11:08:39 AM
Long-term archiving on: : Friday, September 5, 2014 - 11:40:49 AM

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  • HAL Id : hal-01001991, version 1

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Madalina Olteanu, Nathalie Vialaneix. Self-Organizing Maps for clustering and visualization of bipartite graphs. 46e Journées de Statistique de la SFdS, Jun 2014, Rennes, France. pp.109. ⟨hal-01001991⟩

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