Constrained Clustering : Advances in Algorithms, Theory, and Applications

Sugato Basu

ISBN 10: 1584889969 / ISBN 13: 9781584889960

Published by Taylor & Francis Inc Aug 2008, 2008

About this Item

Neuware - Covers the capabilities and limitations of constrained clustering. This title presents various types of constraints for clustering, describes useful variations of the standard problem of clustering under constraints, and applies clustering with constraints to relational, bibliographic, and video data.

Seller Inventory # 9781584889960

Bibliographic Details

• Title: Constrained Clustering : Advances in Algorithms, Theory, and Applications
• Publisher: Taylor & Francis Inc Aug 2008
• Publication Date: 2008
• Language: English
• ISBN 10: 1584889969
• ISBN 13: 9781584889960
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 157H x 29D millimeters 241 millimeters width by 157 millimeters height by 29 millimeters depth
• Weight: 786 grams

About this title

Synopsis

Since 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.

Algorithms

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.

Theory

It also describes variations of the traditional clustering under constraints problem as well as approximation algorithms with helpful performance guarantees.

Applications

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.

About the Author

Sugato Basu, Ian Davidson, Kiri Wagstaff