TY  - BOOK
AU  - Xu,Rui
AU  - Wunsch,Donald C.
ED  - IEEE Computational Intelligence Society.
TI  - Clustering
T2  - IEEE Press series on computational intelligence
SN  - 9780470276808
AV  - QA278 .X8 2009
U1  - 519.5/3 23
PY  - 2009///
CY  - Hoboken, N.J., Piscataway, NJ
PB  - Wiley, IEEE Press
KW  - Cluster analysis
KW  - Cluster Analysis
KW  - Classification automatique (Statistique)
KW  - Cluster <Rechnernetz>
KW  - swd
KW  - Cluster-Analyse
KW  - Mathematical Models in Operations Research II
KW  - AMAT 168
N1  - 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
UR  - http://swbplus.bsz-bw.de/bsz288288696cov.htm
UR  - http://www.gbv.de/dms/ilmenau/toc/569686016.PDF
UR  - http://www.loc.gov/catdir/enhancements/fy1111/2011414051-b.html
UR  - http://www.loc.gov/catdir/enhancements/fy1111/2011414051-d.html
UR  - http://www.loc.gov/catdir/enhancements/fy1111/2011414051-t.html
ER  -