Constrained Clustering
Advances in Algorithms, Theory, and Applications
1st Edition
Published on 18. August 2008
472 pages
978-1-040-20570-9 (ISBN)
Description
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.
Reviews / Votes
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
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
ISBN-13
978-1-040-20570-9 (9781040205709)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Sugato Basu | Ian Davidson | Kiri Wagstaff
Constrained Clustering
Advances in Algorithms, Theory, and Applications
Book
08/2008
1st Edition
Chapman & Hall/CRC
€170.84
Shipment within 15-20 days
Persons
Sugato Basu, Ian Davidson, Kiri Wagstaff
Content
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.
