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.

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Yes, you can access Constrained Clustering by Sugato Basu, Ian Davidson, Kiri Wagstaff in PDF and/or ePUB format, as well as other popular books in Economics & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Chapman and Hall/CRC

Year

2008

ISBN

9781584889977

Edition

Topic

Economics

Subtopic

Statistics for Business & Economics

Index

Economics

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