Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Normal view MARC view ISBD view

Robust cluster analysis and variable selection / Gunter Ritter.

By: Ritter, Gunter.
Material type: TextTextSeries: Monographs on statistics and applied probability ; 137.Publisher: Boca Raton : CRC Press, c2015Description: xx, 371 p. : illustrations ; 27 cm.ISBN: 9781439857960 (hardcover : alk. paper).Subject(s): Cluster analysisDDC classification: 000SA.072
Contents:
1. Mixture and classification models and their likelihood estimators -- 2. Robustification by trimming -- 3. Algorithms -- 4. Favorite solutions and cluster validation -- 5. Variable selection in clustering -- 6. Applications-- Appendices-- References-- Index.
Summary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.072 R614 (Browse shelf) Available 136193
Total holds: 0

Includes bibliographical references and index.

1. Mixture and classification models and their likelihood estimators --
2. Robustification by trimming --
3. Algorithms --
4. Favorite solutions and cluster validation --
5. Variable selection in clustering --
6. Applications--
Appendices--
References--
Index.

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.

There are no comments for this item.

Log in to your account to post a comment.
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in


Visitor Counter