Constrained Clustering

Un libro in lingua di Basu Sugato (EDT) Davidson Ian (EDT) Wagstaff Kiri (EDT) edito da Taylor & Francis, 2008

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Clustering algorithms take data with any number of dimensions and group them into subsets so each member of a subset is near the other members in some sense. In 17 articles including an introduction, contributors describe this phenomenon, focusing on semi-supervise clustering with user feedback, Gaussian mixture models with equivalence constraints, pairwise constraints as priors in probabilistic clustering, clustering with constraints using a mean-field approximation perspectives, constraint-driven co-clustering of 0/1 data, supervised clustering for creating categorization segmentations, clustering with balancing constraints, assignment constraints that avoid empty clusters in k-means clustering, collective relational clustering, non-redundant data clustering, joint cluster analysis of attribute data and relationship data, correlation clustering, interactive visualization for relational data, distance metric learning, data publishing that preserves privacy, and learning with pairwise constraints for video object classification. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)

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