Clustering / IEEE Press Series on Computational Intelligence (PDF)

PREFACE. 1. CLUSTER ANALYSIS. 1.1. Classifi cation and Clustering. 1.2. Defi nition 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. Defi nition 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. 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.

Rui Xu, PhD, is a Research Associate in the Department of
Electrical and Computer Engineering at Missouri University of
Science and Technology. His research interests include
computational intelligence, machine learning, data mining, neural
networks, pattern classification, clustering, and bioinformatics.
Dr. Xu is a member of the IEEE, the IEEE Computational Intelligence
Society (CIS), and Sigma Xi.

Donald C. Wunsch II, PhD, is the M.K. Finley Missouri
Distinguished Professor at Missouri University of Science and
Technology. His key contributions are in adaptive resonance and
reinforcement learning hardware and applications, neurofuzzy
regression, improved Traveling Salesman Problem heuristics,
clustering, and bioinformatics. He is an IEEE Fellow, the 2005
International Neural Networks Society (INNS) President, and Senior
Fellow of the INNS.

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