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The Resource Clustering, Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor, (electronic resource)

Clustering, Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor, (electronic resource)

Work
Publication
Contents
  • COVER
  • CONTENTS
  • PREFACE
  • 1. CLUSTER ANALYSIS
  • 1.1. Classification and Clustering
  • 1.2. Definition of Clusters
  • 1.3. Clustering Applications
  • 1.4. Literature of Clustering Algorithms
  • 1.5. Outline of the Book
  • 2. PROXIMITY MEASURES
  • 2.1. Introduction
  • 2.2. Feature Types and Measurement Levels
  • 2.3. Definition of Proximity Measures
  • 2.4. Proximity Measures for Continuous Variables
  • 2.5. Proximity Measures for Discrete Variables
  • 2.6. Proximity Measures for Mixed Variables
  • 2.7. Summary
  • 3. HIERARCHICAL CLUSTERING
  • 3.1. Introduction
  • 3.2. Agglomerative Hierarchical Clustering
  • 3.3. Divisive Hierarchical Clustering
  • 3.4. Recent Advances
  • 3.5. Applications
  • 3.6. Summary
  • 4. PARTITIONAL CLUSTERING
  • 4.1. Introduction
  • 4.2. Clustering Criteria
  • 4.3. K-Means Algorithm
  • 4.4. Mixture Density-Based Clustering
  • 4.5. Graph Theory-Based Clustering
  • 4.6. Fuzzy Clustering
  • 4.7. Search Techniques-Based Clustering Algorithms
  • 4.8. Applications
  • 4.9. Summary
  • 5. NEURAL NETWORK-BASED CLUSTERING
  • 5.1. Introduction
  • 5.2. Hard Competitive Learning Clustering
  • 5.3. Soft Competitive Learning Clustering
  • 5.4. Applications
  • 5.5. Summary
  • 6. KERNEL-BASED CLUSTERING
  • 6.1. Introduction
  • 6.2. Kernel Principal Component Analysis
  • 6.3. Squared-Error-Based Clustering with Kernel Functions
  • 6.4. Support Vector Clustering
  • 6.5. Applications
  • 6.6. Summary
  • 7. SEQUENTIAL DATA CLUSTERING
  • 7.1. Introduction
  • 7.2. Sequence Similarity
  • 7.3. Indirect Sequence Clustering
  • 7.4. Model-Based Sequence Clustering
  • 7.5. Applications-Genomic and Biological Sequence Clustering
  • 7.6. Summary
  • 8. LARGE-SCALE DATA CLUSTERING
  • 8.1. Introduction
  • 8.2. Random Sampling Methods
  • 8.3. Condensation-Based Methods
  • 8.4. Density-Based Methods
  • 8.5. Grid-Based Methods
  • 8.6. Divide and Conquer
  • 8.7. Incremental Clustering
  • 8.8. Applications
  • 8.9. Summary
  • 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING
  • 9.1. Introduction
  • 9.2. Linear Projection Algorithms
  • 9.3. Nonlinear Projection Algorithms
  • 9.4. Projected and Subspace Clustering
  • 9.5. Applications
  • 9.6. Summary
  • 10. CLUSTER VALIDITY
  • 10.1. Introduction
  • 10.2. External Criteria
  • 10.3. Internal Criteria
  • 10.4. Relative Criteria
  • 10.5. Summary
  • 11. CONCLUDING REMARKS
  • PROBLEMS
  • REFERENCES
  • AUTHOR INDEX
  • SUBJECT INDEX
Isbn
9780470276808
Instance
Label
Clustering
Title
Clustering
Statement of responsibility
Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor
Creator
Contributor
Subject
Language
eng
Related
Member of
Cataloging source
UKM
http://library.link/vocab/creatorName
Xu, Rui
Dewey number
519.5/3
LC call number
QA278
LC item number
.X8 2009
http://library.link/vocab/relatedWorkOrContributorName
  • Wunsch, Donald C
  • IEEE Computational Intelligence Society
Series statement
IEEE Press series on computational intelligence
http://library.link/vocab/subjectName
  • Cluster analysis
  • Cluster Analysis
  • Cluster analysis
  • Classification automatique (Statistique)
  • Cluster <Rechnernetz>
  • Cluster-Analyse
Label
Clustering, Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor, (electronic resource)
Instantiates
Publication
Bibliography note
Includes bibliographical references (p. 293-330) and indexes
Contents
COVER -- CONTENTS -- PREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classification and Clustering -- 1.2. Definition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Definition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications-Genomic and Biological Sequence Clustering -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING -- 9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX
Control code
OCM1bookssj0000123924
Dimensions
unknown
Isbn
9780470276808
Lccn
2011414051
Specific material designation
remote
System control number
(WaSeSS)bookssj0000123924
Label
Clustering, Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor, (electronic resource)
Publication
Bibliography note
Includes bibliographical references (p. 293-330) and indexes
Contents
COVER -- CONTENTS -- PREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classification and Clustering -- 1.2. Definition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Definition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications-Genomic and Biological Sequence Clustering -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING -- 9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX
Control code
OCM1bookssj0000123924
Dimensions
unknown
Isbn
9780470276808
Lccn
2011414051
Specific material designation
remote
System control number
(WaSeSS)bookssj0000123924

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