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.
Introduction
Mixture and classification models and their likelihood estimators
General consistency and asymptotic normality
Local likelihood estimates
Maximum likelihood estimates
Notes
Mixture models and their likelihood estimators
Latent distributions
Finite mixture models
Identifiable mixture models
Asymptotic properties of local likelihood maxima
Asymptotic properties of the MLE: constrained nonparametric mixture models
Asymptotic properties of the MLE: constrained parametric mixture models
Notes
Classification models and their criteria
Probabilistic criteria for general populations
Admissibility and size constraints
Steady partitions
Elliptical models
Normal models
Geometric considerations
Consistency of the MAP criterion
Notes
Robustification by trimming
Outliers and measures of robustness
Outliers
The sensitivities
Sensitivity of ML estimates of mixture models
Breakdown points
Trimming the mixture model
Trimmed likelihood function of the mixture model
Normal components
Universal breakdown points of covariance matrices, mixing rates, and means
Restricted breakdown point of mixing rates and means
Notes
Trimming the classification model – the TDC
Trimmed MAP classification model
Normal case – the Trimmed Determinant Criterion, TDC
Breakdown robustness of the constrained TDC
Universal breakdown point of covariance matrices and means
Restricted breakdown point of the means
Notes
Algorithms
EM algorithm for mixtures
General mixtures
Normal mixtures
Mixtures of multivariate t-distributions
Trimming – the EMT algorithm
Order of Convergence
Acceleration of the mixture EM
Notes
k-Parameters algorithms
General and elliptically symmetric models
Steady solutions and trimming
Using combinatorial optimization
Overall algorithms
Notes
Hierarchical methods for initial solutions
Favorite solutions and cluster validation
Scale balance and Pareto solutions
Number of components of uncontaminated data
Likelihood–ratio tests
Using cluster criteria as test statistics
Model selection criteria
Ridgeline manifold
Number of components and outliers
Classification trimmed likelihood curves
Trimmed BIC
Adjusted BIC
Cluster validation
Separation indices
Normality and related tests
Visualization
Measures of agreement of partitions
Stability
Notes
Variable selection in clustering
Irrelevance
Definition and general properties
The normal case
Filters
Univariate filters
Multivariate filters
Wrappers
Using the likelihood ratio test
Using Bayes factors and their BIC approximations
Maximum likelihood subset selection
Consistency of the MAP cluster criterion with variable selection
Practical guidelines
Notes
Applications
Miscellaneous data sets
IRIS data
SWISS BILLS
STONE FLAKES
Gene expression data
Supervised and unsupervised methods
Combining gene selection and profile clustering
Application to the LEUKEMIA data
Notes
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
Overview
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 ...