Constrained Clustering

Advances in Algorithms, Theory, and Applications

Sugato Basu, Ian Davidson, Kiri Wagstaff, Sugato Basu, Ian Davidson, Kiri Wagstaff

• Publisher: Chapman and Hall/CRC
• Year: 2008
• Print ISBN: 9781584889960
• eBook ISBN: 9781584889977
• Edition: 1
• Topic: Economics
• Subtopic: Statistics for Business & Economics
• Index: Economics

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.

Table of contents

• Cover
• Title
• Copyright
• Foreword
• Editor Biographies
• Contributors
• List of Tables
• List of Figures
• Contents
• Chapter 1: Introduction
• Chapter 2: Semi-Supervised Clustering with User Feedback
• Chapter 3: Gaussian Mixture Models with Equivalence Constraints
• Chapter 4: Pairwise Constraints as Priors in Probabilistic Clustering
• Chapter 5: Clustering with Constraints: A Mean-Field Approximation Perspective
• Chapter 6: Constraint-Driven Co-Clustering of 0/1 Data
• Chapter 7: On Supervised Clustering for Creating Categorization Segmentations
• Chapter 8: Clustering with Balancing Constraints
• Chapter 9: Using Assignment Constraints to Avoid Empty Clusters in k-Means Clustering
• Chapter 10: Collective Relational Clustering
• Chapter 11: Non-Redundant Data Clustering
• Chapter 12: Joint Cluster Analysis of Attribute Data and Relationship Data
• Chapter 13: Correlation Clustering
• Chapter 14: Interactive Visual Clustering for Relational Data
• Chapter 15: Distance Metric Learning from Cannot-be-Linked Example Pairs, with Application to Name Disambiguation
• Chapter 16: Privacy-Preserving Data Publishing: A Constraint-Based Clustering Approach
• Chapter 17: Learning with Pairwise Constraints for Video Object Classification
• Index