Constrained Clustering: Advances in Algorithms, Theory, and Applications / Edition 1

by Sugato Basu, Ian Davidson, Kiri Wagstaff

Overview

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.

Product Details

ISBN-13:	9781584889960
Publisher:	Taylor & Francis
Publication date:	08/18/2008
Series:	Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Pages:	470
Product dimensions:	6.40(w) x 9.30(h) x 1.10(d)

About the Author

Sugato Basu, Ian Davidson, Kiri Wagstaff

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.

Editorial 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

From the Publisher