Constrained Clustering: Advances in Algorithms, Theory, and Applications

by Sugato Basu (Editor), Ian Davidson (Editor), Kiri Wagstaff (Editor)

Overview

This volume encompasses many new types of constraints and clustering methods as well as delivers thorough coverage of the capabilities and limitations of constrained clustering. With contributions from industrial researchers and leading academic experts who pioneered the field, it provides a well-balanced combination of theoretical advances, key algorithmic development, and novel applications. The book presents various types of constraints for clustering and describes useful variations of the standard problem of clustering under constraints. It also demonstrates the application of clustering with constraints to relational, bibliographic, and video data.

Product Details

ISBN-13:	9781040205709
Publisher:	CRC Press
Publication date:	08/18/2008
Series:	Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	472
File size:	9 MB

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