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Identifying Overlying Group of People through Clustering

Identifying Overlying Group of People through Clustering

P. Manimaran, K. Duraiswamy
Copyright: © 2012 |Volume: 7 |Issue: 4 |Pages: 11
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781466612945|DOI: 10.4018/jitwe.2012100104
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MLA

Manimaran, P., and K. Duraiswamy. "Identifying Overlying Group of People through Clustering." IJITWE vol.7, no.4 2012: pp.50-60. http://doi.org/10.4018/jitwe.2012100104

APA

Manimaran, P. & Duraiswamy, K. (2012). Identifying Overlying Group of People through Clustering. International Journal of Information Technology and Web Engineering (IJITWE), 7(4), 50-60. http://doi.org/10.4018/jitwe.2012100104

Chicago

Manimaran, P., and K. Duraiswamy. "Identifying Overlying Group of People through Clustering," International Journal of Information Technology and Web Engineering (IJITWE) 7, no.4: 50-60. http://doi.org/10.4018/jitwe.2012100104

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Abstract

Folksonomies like Delicious and LastFm are modeled as multilateral (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, users have multiple relevant interests and same resource is often tagged with semantically different tags. Few attempts to perceive overlapping communities work on forecasts of hypergraph, which results in momentous loss of information contained in original tripartite structure. Propose first algorithm to detect overlapping communities in folksonomies using complete hypergraph structure. The authors’ algorithm converts a hypergraph into its parallel line graph, using measures of hyperedge similarity, whereby any community detection algorithm on unipartite graphs can be used to produce intersecting communities in folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, demonstrate that proposed algorithm can detect better community structures as compared to existing state-of-the-art algorithms for folksonomies.

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