Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, centre-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Suitable as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining.
Preface; Part I. Clustering, Data and Similarity Measures: 1. Data clustering; 2. DataTypes; 3. Scale conversion; 4. Data standardization and transformation; 5. Data visualization; 6. Similarity and dissimilarity measures; Part II. Clustering Algorithms: 7. Hierarchical clustering techniques; 8. Fuzzy clustering algorithms; 9. Center Based Clustering Algorithms; 10. Search based clustering algorithms; 11. Graph based clustering algorithms; 12. Grid based clustering algorithms; 13. Density based clustering algorithms; 14. Model based clustering algorithms; 15. Subspace clustering; 16. Miscellaneous algorithms; 17. Evaluation of clustering algorithms; Part III. Applications of Clustering: 18. Clustering gene expression data; Part IV. Matlab and C++ for Clustering: 19. Data clustering in Matlab; 20. Clustering in C/C++; A. Some clustering algorithms; B. Thekd-tree data structure; C. Matlab Codes; D. C++ Codes; Subject index; Author index.