Clustering - Info and Reading Options

"Clustering" Table Of Contents:

• 1- COVER
• 2- CONTENTS
• 3- PREFACE
• 4- 1. CLUSTER ANALYSIS
• 5- 1.1. Classification and Clustering
• 6- 1.2. Definition of Clusters
• 7- 1.3. Clustering Applications
• 8- 1.4. Literature of Clustering Algorithms
• 9- 1.5. Outline of the Book
• 10- 2. PROXIMITY MEASURES
• 11- 2.1. Introduction
• 12- 2.2. Feature Types and Measurement Levels
• 13- 2.3. Definition of Proximity Measures
• 14- 2.4. Proximity Measures for Continuous Variables
• 15- 2.5. Proximity Measures for Discrete Variables
• 16- 2.6. Proximity Measures for Mixed Variables
• 17- 2.7. Summary
• 18- 3. HIERARCHICAL CLUSTERING
• 19- 3.1. Introduction
• 20- 3.2. Agglomerative Hierarchical Clustering
• 21- 3.3. Divisive Hierarchical Clustering
• 22- 3.4. Recent Advances
• 23- 3.5. Applications
• 24- 3.6. Summary
• 25- 4. PARTITIONAL CLUSTERING
• 26- 4.1. Introduction
• 27- 4.2. Clustering Criteria
• 28- 4.3. K-Means Algorithm
• 29- 4.4. Mixture Density-Based Clustering
• 30- 4.5. Graph Theory-Based Clustering
• 31- 4.6. Fuzzy Clustering
• 32- 4.7. Search Techniques-Based Clustering Algorithms
• 33- 4.8. Applications
• 34- 4.9. Summary
• 35- 5. NEURAL NETWORK-BASED CLUSTERING
• 36- 5.1. Introduction
• 37- 5.2. Hard Competitive Learning Clustering
• 38- 5.3. Soft Competitive Learning Clustering
• 39- 5.4. Applications
• 40- 5.5. Summary
• 41- 6. KERNEL-BASED CLUSTERING
• 42- 6.1. Introduction
• 43- 6.2. Kernel Principal Component Analysis
• 44- 6.3. Squared-Error-Based Clustering with Kernel Functions
• 45- 6.4. Support Vector Clustering
• 46- 6.5. Applications
• 47- 6.6. Summary
• 48- 7. SEQUENTIAL DATA CLUSTERING
• 49- 7.1. Introduction
• 50- 7.2. Sequence Similarity
• 51- 7.3. Indirect Sequence Clustering
• 52- 7.4. Model-Based Sequence Clustering
• 53- 7.5. Applications-Genomic and Biological Sequence Clustering
• 54- 7.6. Summary
• 55- 8. LARGE-SCALE DATA CLUSTERING
• 56- 8.1. Introduction
• 57- 8.2. Random Sampling Methods
• 58- 8.3. Condensation-Based Methods
• 59- 8.4. Density-Based Methods
• 60- 8.5. Grid-Based Methods
• 61- 8.6. Divide and Conquer
• 62- 8.7. Incremental Clustering
• 63- 8.8. Applications
• 64- 8.9. Summary
• 65- 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING
• 66- 9.1. Introduction
• 67- 9.2. Linear Projection Algorithms
• 68- 9.3. Nonlinear Projection Algorithms
• 69- 9.4. Projected and Subspace Clustering
• 70- 9.5. Applications
• 71- 9.6. Summary
• 72- 10. CLUSTER VALIDITY
• 73- 10.1. Introduction
• 74- 10.2. External Criteria
• 75- 10.3. Internal Criteria
• 76- 10.4. Relative Criteria
• 77- 10.5. Summary
• 78- 11. CONCLUDING REMARKS
• 79- PROBLEMS
• 80- REFERENCES
• 81- AUTHOR INDEX
• 82- SUBJECT INDEX.

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