2008 ConstrainedClustering

• (Basu et al., 2008) ⇒ Sugato Basu, Ian Davidson, Kiri Wagstaff;. (2008). “Constrained Clustering: Advances in Algorithms, Theory, and Applications.” Chapman & Hall. ISBN:1584889969

Subject Headings: Clustering Discipline, Edited Volume.

Quotes

Book 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.

Table of Contents

Introduction. (2008). Sugato Basu, Ian Davidson, and Kiri L. Wagstaff
Semisupervised Clustering with User Feedback. (2008). David Cohn, Rich Caruana, and Andrew McCallum
Gaussian Mixture Models with Equivalence Constraints. (2008). Noam Shental, Aharon Bar-Hillel, Tomer Hertz, and Daphna Weinshall
Pairwise Constraints as Priors in Probabilistic Clustering. (2008). Zhengdong Lu and Todd K. Leen
Clustering with Constraints: A Mean-Field Approximation Perspective. (2008). Tilman Lange, Martin H. Law, Anil K. Jain, and J.M. Buhmann
Constraint-Driven Co-Clustering of 0/1 Data. (2008). Ruggero G. Pensa, Céline Robardet, and Jean-François Boulicaut
On Supervised Clustering for Creating Categorization Segmentations. (2008). Charu Aggarwal, Stephen C. Gates, and Philip S. Yu
Clustering with Balancing Constraints. (2008). Arindam Banerjee and Joydeep Ghosh
Using Assignment Constraints to Avoid Empty Clusters in k-Means Clustering. (2008). A. Demiriz, K.P. Bennett, and P.S. Bradley
Collective Relational Clustering. (2008). Indrajit Bhattacharya and Lise Getoor
Nonredundant Data Clustering. (2008). David Gondek
Joint Cluster Analysis of Attribute Data and Relationship Data. (2008). Martin Ester, Rong Ge, Byron J. Gao, Zengjian Hu, and Boaz Ben-moshe
Correlation Clustering. (2008). Nicole Immorlica and Anthony Wirth
Interactive Visual Clustering for Relational Data. (2008). Marie desJardins, James MacGlashan, and Julia Ferraioli
Distance Metric Learning from Cannot-Be-Linked Example Pairs with Application to Name Disambiguation. (2008). Satoshi Oyama and Katsumi Tanaka
Privacy-Preserving Data Publishing: A Constraint-based Clustering Approach. (2008). Anthony K.H. Tung, Jiawei Han, Laks V.S. Lakshmanan, and Raymond T. Ng
Learning with Pairwise Constraints for Video Object Classification. (2008). Rong Yan, Jian Zhang, Jie Yang, and Alexander G. Hauptmann,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ConstrainedClustering	Sugato Basu Kiri Wagstaff Ian Davidson			Constrained Clustering: Advances in Algorithms, Theory, and Applications			http://books.google.com/books?id=GMAkzEWlJzsC			2008