RT Book, Section
T1 A divide-link algorithm based on fuzzy similarity for clustering networks
A1 Gómez González, Daniel
A1 Montero De Juan, Francisco Javier
A1 Yáñez Gestoso, Francisco Javier
AB 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.
PB IEEE
SN 978-1-4577-1676-8
YR 2011
FD 2011
LK https://hdl.handle.net/20.500.14352/45540
UL https://hdl.handle.net/20.500.14352/45540
LA eng
DS Docta Complutense
RD 6 abr 2025