Clustering /
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Author / Creator: | Xu, Rui. |
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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: | |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/8680198 |
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