The paper presents a novel scheme for graph-based clusteringwith the goal of identifying groups of related tags in folksonomies.The proposed scheme searches for core sets, i.e. groups of nodes thatare densely connected to each other by efficiently exploring the twodimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. We evaluate this scheme on three real-world tag networks by assessing the relatedness of same-cluster tags and by using tag clusters for tag recommendation. In addition, we compare our results to the ones derived from a baseline graph-based clustering method and from a popular modularity maximization clustering method.
%B Proceedings of the 12th international conference on Data warehousing and knowledge discovery %S DaWaK’10 %I Springer-Verlag %C Berlin, Heidelberg %P 65–76 %@ 3-642-15104-3, 978-3-642-15104-0 %G eng %0 Conference Paper %D 2010 %T A Graph-Based Clustering Scheme for Identifying Related Tags in Folksonomies %XThe paper presents a novel scheme for graph-based clusteringwith the goal of identifying groups of related tags in folksonomies.The proposed scheme searches for core sets, i.e. groups of nodes thatare densely connected to each other by efficiently exploring the twodimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. We evaluate this scheme on three real-world tag networks by assessing the relatedness of same-cluster tags and by using tag clusters for tag recommendation. In addition, we compare our results to the ones derived from a baseline graph-based clustering method and from a popular modularity maximization clustering method.
%G eng %0 Journal Article %J I. J. Medical Informatics %D 2007 %T Gene functional annotation by statistical analysis of biomedical articles %A Theodosiou, Theodosios %A Angelis, Lefteris %A Athena Vakali %A Thomopoulos, G. N. %B I. J. Medical Informatics %V 76 %P 601-613 %G eng %0 Conference Paper %D 2007 %T GRANULAR GRAPH CLUSTERING IN THE WEB %XWe investigate the partition of a weighted graph, representing traffic, to a number ofsubgraphs such that both inter(external)-subgraph traffic is minimized and intra(internal)-subgraph traffic is maximized. The long-term objective is Web-navigation support. Wepursue a solution by applying a simple agglomerative clustering algorithm, or ACA forshort, to a metric space emerging from a weighted graph. An enabling technology isinspired from mathematical lattice theory. The proposed techniques compare favorablywith other techniques in an application to a graph stemming from a University Web-site.
%G eng