Robust Cluster Analysis and Variable Selection

Reviews / Votes

"I congratulate the author on his hard work, which provides a well-founded mathematical/probabilistic treatment for some valuable clustering techniques...I highly recommend this book and find it very enjoyable to read for those with enough background and who wish to gain a more in-depth knowledge of cluster analysis. For this audience, it is a stimulating read covering several novel and useful ideas and providing a starting point for developing more advanced theory and applications...In summary, I found the book appealing and I appreciate the effort made by the author in providing a rigorous approach to (robust) cluster analysis."
-Journal of the American Statistical Association, May 2016

"This book is quite theoretical, with parts of the text being highly technical. However, it also provides the practitioner with useful procedures and algorithms applicable without having to understand the probabilistic fundamentals."
-Mathematical Reviews, August 2015

"Professor Ritter has contributed an original and highly valuable volume on probabilistic clustering. This book provides a selection of methods that the author has found most useful in the analysis of real and simulated data. It places a special focus on problems caused by outliers and irrelevant variables, yet, above all, with an admirable and in-depth attention to the roots of methods in mathematical theorems and fundamental statistical principles. An absolute must for those who really care about the mathematical-statistical foundations of probabilistic cluster analysis and who want to get to the bottom of them."
-Iven Van Mechelen, Past President of the International Federation of Classification Societies

"This book provides a marvelous, deep, comprehensive, and knowledgeable presentation of basic and advanced methods and algorithms for data clustering related to model-based approaches (mixture model and fixed-classification approach). Its special and innovative features consist in the presentation of outlier models and corresponding trimming variants of classical maximum likelihood clustering methods and in a complete derivation of the large-sample theory of the resulting estimates, with and without outliers (this did not exist in book form before). In addition to presenting suitable (EM and k-parameters) clustering algorithms, the author proposes new ideas to cope with a possibly large number of 'local' clustering solutions, e.g., by selecting 'favorite' classifications, together with methods for cluster evaluation and variable selection. Real-case examples, e.g., from gene analysis, illustrate the proposed methods.
Given its broad methodological range, the presentation of new concepts and methods in clustering, the consideration of outlier-infected situations, and the complete and exact derivation of results, this book can be considered a standard work for all classificationists and data analysts. For practitioners, it contains a wealth of models and algorithms to choose from and many tricky practical advices for computing and interpretation. For researchers, this will be an indispensable source of information concerning the statistical and mathematical foundations and results in the context of model-based clustering."
-Hans-Hermann Bock, RWTH Aachen University, Germany