Xu, Rui.
Clustering /
Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor.
- Hoboken, N.J. : Piscataway, NJ : Wiley ; IEEE Press, c2009.
- x, 358 p. : ill. ; 25 cm.
- IEEE Press series on computational intelligence .
- IEEE series on computational intelligence. .
Includes bibliographical references (p. 293-330) and indexes.
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.
ISBN: 9780470276808 0470276800
LCCN: 2011414051
Nat. Bib. No.: GBA875868 bnb
Nat. Bib. Agency Control No.: 014641084 Uk
Subjects--Topical Terms: Cluster analysis. Cluster Analysis. Cluster analysis. Classification automatique (Statistique) Cluster Cluster-Analyse.
Index Terms--Curriculum Objective: Mathematical Models in Operations Research II --AMAT 168
LC Class. No.: QA278 / .X8 2009
Dewey Class. No.: 519.5/3