Clustering

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.

“This book provides a comprehensive and thorough presentation of this research area, describing some of the most important clustering algorithms proposed in research literature.” (Computing Reviews, June 2009) "The book covers a lot of ground in a relatively small number of pages, and should work well as a learning tool and reference." (Computing Reviews, May 28, 2009)

• 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. 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.