Table of Contents

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.

About the Author

Sugato Basu, Ian Davidson, Kiri Wagstaff

Reviews

From the Foreword
“… this book shows how constrained clustering can be used to tackle large problems involving textual, relational, and even video data. After reading this book, you will have the tools to be a better analyst [and] to gain more insight from your data, whether it be textual, audio, video, relational, genomic, or anything else.”
—Dr. Peter Norvig, Director of Research, Google, Inc., Mountain View, California, USA