2026-06-26T20:14:57Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/455402024-11-26T14:03:17Zcom_20.500.14352_14col_20.500.14352_21

Gómez González, Daniel Montero De Juan, Francisco Javier Yáñez Gestoso, Francisco Javier 2023-06-20T05:46:30Z 2023-06-20T05:46:30Z 2011 978-1-4577-1676-8 10.1109/ISDA.2011.6121830 https://hdl.handle.net/20.500.14352/45540 https//doi.org/10.1109/ISDA.2011.6121830 http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6121830&abstractAccess=no&userType=inst 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. eng restricted access A divide-link algorithm based on fuzzy similarity for clustering networks book part