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Medientyp: E-Book Titel: Clustering Beteiligte: Xu, Rui [Sonstige Person, Familie und Körperschaft]; Wunsch, Donald C. [Sonstige Person, Familie und Körperschaft] Erschienen: Piscataway, New Jersey; Hoboken, NJ: IEEE Press, Wiley, c2009 Online-Ausg. Erschienen in: IEEE series on computational intelligence IEEE Xplore Digital Library Umfang: Online Ressource (x, 358 p.); ill Sprache: Englisch ISBN: 9780470382783; 0470382783; 9780470382776; 0470382775 RVK-Notation: ST 300 : Allgemeines ST 151 : Parallelrechner Schlagwörter: Cluster Cluster Cluster Art der Reproduktion: Online-Ausg. Entstehung: Anmerkungen: Includes bibliographical references and index Mode of access: World Wide Web Beschreibung: 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