Constrained Clustering: Advances in Algorithms, Theory, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Sugato Basu, Ian Davidson, Kiri Wagstaff

ISBN 10: 1584889969 / ISBN 13: 9781584889960

Published by Chapman and Hall/CRC 2015-02-04, 2015

About this Item

Seller Inventory # 6666-TNFPD-9781584889960

Bibliographic Details

Title: Constrained Clustering: Advances in ...

Publisher: Chapman and Hall/CRC 2015-02-04

Publication Date: 2015

Binding: Hardcover

Condition: New

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