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Robust cluster analysis and variable selection

Author: Gunter Ritter
Publisher: Boca Raton : CRC Press, [2015] ©2015
Series: Monographs on statistics & applied probability (Series), 137.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Gunter Ritter
ISBN: 9781439857977 1439857970 9781439857960 1439857962 9781322624860 1322624860
OCLC Number: 890721110
Description: 1 online resource : text file, PDF.
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.
Series Title: Monographs on statistics & applied probability (Series), 137.
Responsibility: Gunter Ritter, Universität Passau, Germany.

Abstract:

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 bot.

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"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 Read more...

 
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