An edition of Clustering
(2008)
Clustering
by Rui Xu
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Book Details
Table of 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.
Edition Notes
Includes bibliographical references (p. 293-330) and indexes.
Classifications
The Physical Object
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| August 2, 2020 | Edited by ImportBot | import existing book |
| June 29, 2019 | Edited by MARC Bot | import existing book |
| October 23, 2011 | Created by LC Bot | import new book |