Clustering /

Clustering /

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Bibliographic Details
Author / Creator:Xu, Rui.
Imprint:Oxford : Wiley, c2009.
Description:1 online resource (x, 358 p.) : ill.
Language:English
Series:IEEE series on computational intelligence
IEEE series on computational intelligence.
Subject:
Cluster analysis.
Cluster analysis.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8680198
Hidden Bibliographic Details
Other authors / contributors:Wunsch, Donald C.
ISBN:9780470382776
0470382775
9780470382783 (electronic bk.)
0470382783 (electronic bk.)
9780470276808
0470276800
Notes:Includes bibliographical references and index.
Other form:Print version: Xu, Rui. Clustering. Oxford : Wiley, c2009 9780470276808 0470276800
Standard no.:10.1002/9780470382776
Table of Contents:
  • Preface
  • 1. Cluster Analysis
  • 1.1. Classifi cation and Clustering
  • 1.2. Defi nition 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. Defi nition 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
  • 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

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