Constrained Clustering

Constrained Clustering : Advances in Algorithms, Theory, and Applications

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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 more

Product details

  • Hardback | 472 pages
  • 213.36 x 276.86 x 12.7mm | 725.74g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 110 black & white illustrations, 25 black & white tables, 11 black & white halftones
  • 1584889969
  • 9781584889960

About Sugato Basu

Google, Inc. Mountain View, California, USA University of California, Davis, USA Jet Propulsion Laboratory, Pasadena, California, USAshow more

Table of contents

Introduction 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, Celine Robardet, and Jean-Francois 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 David Gondek Joint Cluster Analysis of Attribute Data and Relationship Data Martin Ester, Rong Ge, Byron J. Gao, Zengjian Hu, and Boaz Ben-moshe Correlation Clustering 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 References Indexshow more

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