An evaluation study of clustering algorithms in the scope of user communities assessment

Karamolegkos, PN; Patrikakis, CZ; Doulamis, ND; Vlacheas, PT; Nikolakopoulos, IG

dc.contributor.author	Karamolegkos, PN	en
dc.contributor.author	Patrikakis, CZ	en
dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Vlacheas, PT	en
dc.contributor.author	Nikolakopoulos, IG	en
dc.date.accessioned	2014-03-01T01:29:49Z
dc.date.available	2014-03-01T01:29:49Z
dc.date.issued	2009	en
dc.identifier.issn	0898-1221	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19361
dc.subject	Modeling	en
dc.subject	Performance evaluation	en
dc.subject	Social networking	en
dc.subject	Spectral clustering	en
dc.subject	User profile	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.other	Modeling	en
dc.subject.other	Performance evaluation	en
dc.subject.other	Social networking	en
dc.subject.other	Spectral clustering	en
dc.subject.other	User profile	en
dc.subject.other	Heuristic algorithms	en
dc.subject.other	Magnets	en
dc.subject.other	Optimization	en
dc.subject.other	Clustering algorithms	en
dc.title	An evaluation study of clustering algorithms in the scope of user communities assessment	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.camwa.2009.05.023	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.camwa.2009.05.023	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	In this paper, we provide the results of ongoing work in Magnet Beyond project, regarding social networking services. We introduce an integrated social networking framework through the definition or the appropriate notions and metrics. This allows one to run an evaluation study of three widely used clustering methods (k-means, hierarchical and spectral clustering) in the scope of social groups assessment and in regard to the cardinality of the profile used to assess users' preferences. Such an evaluation study is performed in the context of our service requirements (i.e. on the basis of equal-sized group formation and of maximization of interests' commonalities between users within each social group). The experimental results indicate that spectral clustering, due to the optimization it offers in terms of normalized cut minimization, is applicable within the context of Magnet Beyond socialization services. Regarding profile's cardinality impact on the system performance, this is shown to be highly dependent on the underlying distribution that characterizes the frequency of user preferences appearance. Our work also incorporates the introduction of a heuristic algorithm that assigns new users that join the service into appropriate social groups, once the service has been initialized and the groups have been assessed using spectral clustering. The results clearly show that our approach is able to adhere to the service requirements as new users join the system, without the need of an iterative spectral clustering application that is computationally demanding. (C) 2009 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computers and Mathematics with Applications	en
dc.identifier.doi	10.1016/j.camwa.2009.05.023	en
dc.identifier.isi	ISI:000270630800002	en
dc.identifier.volume	58	en
dc.identifier.issue	8	en
dc.identifier.spage	1498	en
dc.identifier.epage	1519	en