Robust Cluster Analysis and Variable Selection

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 both applications, describing scenarios in which accuracy and speed are the primary goals.

Robust Cluster Analysis and Variable Selection

includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. The book illustrates the different methods with simulated data and applies them to real-world data sets that can be easily downloaded from the web. This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals.

Table of Contents

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