Gómez González, DanielMontero De Juan, Francisco JavierYáñez Gestoso, Francisco Javier2023-06-202023-06-202011978-1-4577-1676-810.1109/ISDA.2011.6121830https://hdl.handle.net/20.500.14352/45540In 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.engbook parthttps//doi.org/10.1109/ISDA.2011.6121830http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6121830&abstractAccess=no&userType=instrestricted access519.8Graph TheoryCommunity detectionFuzzy SimilarityInvestigación operativa (Matemáticas)1207 Investigación Operativa