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A divide-link algorithm based on fuzzy similarity for clustering networks

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Gómez, D. and Montero, Javier and Yáñez, Javier (2011) A divide-link algorithm based on fuzzy similarity for clustering networks. In Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on. Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on . IEEE, 1247 -1252. ISBN 978-1-4577-1676-8

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6121830&abstractAccess=no&userType=inst


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Abstract

In this paper we present an efficient hierarchical clustering algorithm for relational data, being those relations modeled by a graph. The hierarchical clustering approach proposed in this paper is based on divisive and link criteria, to break the graph and join the nodes at different stages. We then apply this approach to a community detection problems based on the well-known edge line betweenness measure as the divisive criterium and a fuzzy similarity relation as the link criterium. We present also some computational results in some well-known examples like the Karate Zachary club-network, the Dolphins network, Les Miserables network and the Authors centrality network, comparing these results to some standard methodologies for hierarchical clustering problem, both for binary and valued graphs.


Item Type:Book Section
Subjects:Sciences > Mathematics > Operations research
ID Code:28646
Deposited On:26 Feb 2015 07:42
Last Modified:02 Jun 2016 17:02

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