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dc.contributor.authorTadepalli, Satishen_US
dc.contributor.authorRamakrishnan, Narenen_US
dc.contributor.authorWatson, Layne T.en_US
dc.date.accessioned2013-06-19T14:35:53Z
dc.date.available2013-06-19T14:35:53Z
dc.date.issued2009
dc.identifierhttp://eprints.cs.vt.edu/archive/00001076/en_US
dc.identifier.urihttp://hdl.handle.net/10919/20151
dc.description.abstractClustering is the unsupervised method of grouping data samples to form a partition of a given dataset. Such grouping is typically done based on homogeneity assumptions of clusters over an attribute space and hence the precise definition of the similarity metric affects the clusters inferred. In recent years, new formulations of clustering have emerged that posit indirect constraints on clustering, typically in terms of preserving dependencies between data samples and auxiliary variables. These formulations find applications in bioinformatics, web mining, social network analysis, and many other domains. The purpose of this survey is to provide a gentle introduction to these formulations, their mathematical assumptions, and the contexts under which they are applicable.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectArtificial intelligenceen_US
dc.titleClustering constrained by dependenciesen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-09-12en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001076/01/ACMSurv09.pdf


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