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