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.
General
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