CONSTRAINED CLUSTERING: ADVANCES IN ALGORITHMS, THEORY, AND APPLICATIONS

Introduction
Semisupervised Clustering with User Feedback
Gaussian Mixture Models with Equivalence Constraints
Pairwise Constraints as Priors in Probabilistic Clustering
Clustering with Constraints: A Mean-Field Approximation Perspective
Constraint-Driven Co-Clustering of 0/1 Data
On Supervised Clustering for Creating Categorization Segmentations
Clustering with Balancing Constraints
Using Assignment Constraints to Avoid Empty Clusters in k-Means Clustering
Collective Relational Clustering
Nonredundant Data Clustering
Joint Cluster Analysis of Attribute Data and Relationship Data
Correlation Clustering
Interactive Visual Clustering for Relational Data
Distance Metric Learning from Cannot-Be-Linked Example Pairs with Application to Name Disambiguation
Privacy-Preserving Data Publishing: A Constraint-Based Clustering Approach
Learning with Pairwise Constraints for Video Object Classification
References
Index

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.

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