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 probabi-
listic 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 impor-
tant 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 on how to use clustering methods as
well as applicable procedures and algorithms without having to understand
their probabilistic fundamentals.
Robust Cluster Analysis and Variable Selection
Monographs on Statistics and Applied Probability 137