Constrained Clustering
eBook - PDF

Constrained Clustering

Advances in Algorithms, Theory, and Applications

  1. 472 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Constrained Clustering

Advances in Algorithms, Theory, and Applications

About this book

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.

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Yes, you can access Constrained Clustering by Sugato Basu, Ian Davidson, Kiri Wagstaff, Sugato Basu,Ian Davidson,Kiri Wagstaff in PDF and/or ePUB format, as well as other popular books in Economics & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title
  3. Copyright
  4. Foreword
  5. Editor Biographies
  6. Contributors
  7. List of Tables
  8. List of Figures
  9. Contents
  10. Chapter 1: Introduction
  11. Chapter 2: Semi-Supervised Clustering with User Feedback
  12. Chapter 3: Gaussian Mixture Models with Equivalence Constraints
  13. Chapter 4: Pairwise Constraints as Priors in Probabilistic Clustering
  14. Chapter 5: Clustering with Constraints: A Mean-Field Approximation Perspective
  15. Chapter 6: Constraint-Driven Co-Clustering of 0/1 Data
  16. Chapter 7: On Supervised Clustering for Creating Categorization Segmentations
  17. Chapter 8: Clustering with Balancing Constraints
  18. Chapter 9: Using Assignment Constraints to Avoid Empty Clusters in k-Means Clustering
  19. Chapter 10: Collective Relational Clustering
  20. Chapter 11: Non-Redundant Data Clustering
  21. Chapter 12: Joint Cluster Analysis of Attribute Data and Relationship Data
  22. Chapter 13: Correlation Clustering
  23. Chapter 14: Interactive Visual Clustering for Relational Data
  24. Chapter 15: Distance Metric Learning from Cannot-be-Linked Example Pairs, with Application to Name Disambiguation
  25. Chapter 16: Privacy-Preserving Data Publishing: A Constraint-Based Clustering Approach
  26. Chapter 17: Learning with Pairwise Constraints for Video Object Classification
  27. Index