Posted on 2012-04-27, by wowebook.
Sugato Basu, Ian Davidson, Kiri Lou Wagstaff - Constrained Clustering: Advances in Algorithms, Theory, and Applications
Published: 2008-08-18 | ISBN: 1584889969 | DJVU | 472 pages | 4.26 MB
the initial work on constrained clustering, there have been numerous
advances in methods, applications, and our understanding of the
theoretical properties of constraints and constrained clustering
algorithms. Bringing these developments together, Constrained
Clustering: Advances in Algorithms, Theory, and Applications presents an
extensive collection of the latest innovations in clustering data
analysis methods that use background knowledge encoded as constraints.
The first five chapters of this volume investigate advances in the use of instance-level, pairwise constraints for partitional and hierarchical clustering. The book then explores other types of constraints for clustering, including cluster size balancing, minimum cluster size,and cluster-level relational constraints.
It also describes variations of the traditional clustering under constraints problem as well as approximation algorithms with helpful performance guarantees.
The book ends by applying clustering with constraints to relational data, privacy-preserving data publishing, and video surveillance data. It discusses an interactive visual clustering approach, a distance metric learning approach, existential constraints, and automatically generated constraints.
With contributions from industrial researchers and leading academic experts who pioneered the field, this volume delivers thorough coverage of the capabilities and limitations of constrained clustering methods as well as introduces new types of constraints and clustering algorithms.
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