Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Heinrich Fritz, Luis A. García-Escudero, Agustín Mayo-Iscar
Title: tclust: An R Package for a Trimming Approach to Cluster Analysis
Abstract: Outlying data can heavily influence standard clustering methods. At the same time, clustering principles can be useful when robustifying statistical procedures. These two reasons motivate the development of feasible robust model-based clustering approaches. With this in mind, an R package for performing non-hierarchical robust clustering, called tclust, is presented here. Instead of trying to “fit” noisy data, a proportion α of the most outlying observations is trimmed. The tclust package efficiently handles different cluster scatter constraints. Graphical exploratory tools are also provided to help the user make sensible choices for the trimming proportion as well as the number of clusters to search for.

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Paper: tclust: An R Package for a Trimming Approach to Cluster Analysis     Download PDF (Downloads: 2423)
Supplements:
tclust_1.1-02.tar.gz: R source package Download (Downloads: 566; 1007KB)
v47i12.R: R example code from the paper Download (Downloads: 588; 14KB)
DOI: 10.18637/jss.v047.i12

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.