Constrained Clustering: Advances in Algorithms, Theory, and Applications

Sugato Basu, Ian Davidson, Kiri Wagstaff

August 18, 2008 by Chapman and Hall/CRC
Reference - 472 Pages - 110 B/W Illustrations
ISBN 9781584889960 - CAT# C9969
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Features

  • Provides a well-balanced combination of theoretical advances, key algorithmic development, and novel applications
  • Presents various types of constraints for clustering
  • Describes useful variations of the standard problem of clustering under constraints
  • Applies clustering with constraints to different domains, such as analyzing relational, bibliographic, and video data
  • Summary

    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.