Ebook: Robust cluster analysis and variable selection

Front Cover; Contents; Preface; List of Figures; List of Tables; Glossary of notation; Introduction; Chapter 1 -- Mixture and classification models and their likelihood estimators; Chapter 2 -- Robustification by trimming; Chapter 3 -- Algorithms; Chapter 4 -- Favorite solutions and cluster validation; Chapter 5 -- Variable selection in clustering; Chapter 6 -- Applications; Appendix A -- Geometry and linear algebra; Appendix B -- Topology; Appendix C -- Analysis; Appendix D -- Measures and probabilities; Appendix E -- Probability; Appendix F -- Statistics; Appendix G -- Optimization; References.;Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of bot.