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Getting Clusters from Structure Data and Attribute Data

Abstract : If the clustering task is widely studied both in graph clustering and in non supervised learning, combined clustering which exploits simultaneously the relationships between the ver- tices and attributes describing them, is quite new. In this paper, we present different scenarios for this task and, we evaluate their performances and their results on a dataset, with ground truth, built from several sources and containing a scientific social network in which textual data is associated to each vertex and the classes are known. We argue that, depending on the kind of data we have and the type of results we want, the choice of the clustering method is important and we present some concrete examples for underlining this.
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Contributor : David Combe <>
Submitted on : Saturday, September 8, 2012 - 12:16:52 AM
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David Combe, Christine Largeron, Előd Egyed-Zsigmond, Mathias Géry. Getting Clusters from Structure Data and Attribute Data. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2012, Istanbul, Turkey. pp.731-733, ⟨10.1109/ASONAM.2012.123⟩. ⟨hal-00730224⟩



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