Clustering
By: Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor
Contributors: Wunsch, Donald C.; IEEE Computational Intelligence Society
Series: IEEE Press series on computational intelligence; IEEE series on computational intelligence
Publisher: Wiley; IEEE Press
Published: 2009
Place of publication: Hoboken, N.J; Piscataway, NJ
Language: eng
Number of pages: 358
ISBN-13: 9780470276808
Subjects: Cluster analysis; Cluster Analysis; Cluster <Rechnernetz>; Classification automatique (Statistique); Cluster-Analyse
Notes: Includes bibliographical references (p. 293-330) and indexes.
Table of contents: title: COVER; title: CONTENTS; title: PREFACE; title: 1. CLUSTER ANALYSIS; title: 1.1. Classification and Clustering; title: 1.2. Definition of Clusters; title: 1.3. Clustering Applications; title: 1.4. Literature of Clustering Algorithms; title: 1.5. Outline of the Book; title: 2. PROXIMITY MEASURES; title: 2.1. Introduction; title: 2.2. Feature Types and Measurement Levels; title: 2.3. Definition of Proximity Measures; title: 2.4. Proximity Measures for Continuous Variables; title: 2.5. Proximity Measures for Discrete Variables; title: 2.6. Proximity Measures for Mixed Variables; title: 2.7. Summary; title: 3. HIERARCHICAL CLUSTERING; title: 3.1. Introduction; title: 3.2. Agglomerative Hierarchical Clustering; title: 3.3. Divisive Hierarchical Clustering; title: 3.4. Recent Advances; title: 3.5. Applications; title: 3.6. Summary; title: 4. PARTITIONAL CLUSTERING; title: 4.1. Introduction; title: 4.2. Clustering Criteria; title: 4.3. K-Means Algorithm; title: 4.4. Mixture Density-Based Clustering; title: 4.5. Graph Theory-Based Clustering; title: 4.6. Fuzzy Clustering; title: 4.7. Search Techniques-Based Clustering Algorithms; title: 4.8. Applications; title: 4.9. Summary; title: 5. NEURAL NETWORK-BASED CLUSTERING; title: 5.1. Introduction; title: 5.2. Hard Competitive Learning Clustering; title: 5.3. Soft Competitive Learning Clustering; title: 5.4. Applications; title: 5.5. Summary; title: 6. KERNEL-BASED CLUSTERING; title: 6.1. Introduction; title: 6.2. Kernel Principal Component Analysis; title: 6.3. Squared-Error-Based Clustering with Kernel Functions; title: 6.4. Support Vector Clustering; title: 6.5. Applications; title: 6.6. Summary; title: 7. SEQUENTIAL DATA CLUSTERING; title: 7.1. Introduction; title: 7.2. Sequence Similarity; title: 7.3. Indirect Sequence Clustering; title: 7.4. Model-Based Sequence Clustering; title: 7.5. Applications-Genomic and Biological Sequence Clustering; title: 7.6. Summary; title: 8. LARGE-SCALE DATA CLUSTERING; title: 8.1. Introduction; title: 8.2. Random Sampling Methods; title: 8.3. Condensation-Based Methods; title: 8.4. Density-Based Methods; title: 8.5. Grid-Based Methods; title: 8.6. Divide and Conquer; title: 8.7. Incremental Clustering; title: 8.8. Applications; title: 8.9. Summary; title: 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING; title: 9.1. Introduction; title: 9.2. Linear Projection Algorithms; title: 9.3. Nonlinear Projection Algorithms; title: 9.4. Projected and Subspace Clustering; title: 9.5. Applications; title: 9.6. Summary; title: 10. CLUSTER VALIDITY; title: 10.1. Introduction; title: 10.2. External Criteria; title: 10.3. Internal Criteria; title: 10.4. Relative Criteria; title: 10.5. Summary; title: 11. CONCLUDING REMARKS; title: PROBLEMS; title: REFERENCES; title: AUTHOR INDEX; title: SUBJECT INDEX.