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Zusatztext 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 Informationen zum Autor Sugato Basu, Ian Davidson, Kiri Wagstaff Klappentext Covers the capabilities and limitations of constrained clustering. This title presents various types of constraints for clustering! describes useful variations of the standard problem of clustering under constraints! and applies clustering with constraints to relational! bibliographic! and video data. Zusammenfassung Covers the capabilities and limitations of constrained clustering. This title presents various types of constraints for clustering, describes useful variations of the standard problem of clustering under constraints, and applies clustering with constraints to relational, bibliographic, and video data. Inhaltsverzeichnis 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! 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 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 Index ...
