rCOSA: A Software Package for Clustering Objects on Subsets of Attributes

Maarten M. Kampert (), Jacqueline J. Meulman and Jerome H. Friedman
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Maarten M. Kampert: Leiden University
Jacqueline J. Meulman: Leiden University
Jerome H. Friedman: Stanford University

Journal of Classification, 2017, vol. 34, issue 3, No 10, 514-547

Abstract: Abstract rCOSA is a software package interfaced to the R language. It implements statistical techniques for clustering objects on subsets of attributes in multivariate data. The main output of COSA is a dissimilarity matrix that one can subsequently analyze with a variety of proximity analysis methods. Our package extends the original COSA software (Friedman and Meulman, 2004) by adding functions for hierarchical clustering methods, least squares multidimensional scaling, partitional clustering, and data visualization. In the many publications that cite the COSA paper by Friedman and Meulman (2004), the COSA program is actually used only a small number of times. This can be attributed to the fact that this original implementation is not very easy to install and use. Moreover, the available software is out-of-date. Here, we introduce an up-to-date software package and a clear guidance for this advanced technique. The software package and related links are available for free at: https://github.com/mkampert/rCOSA .

Keywords: Distance-based clustering; Subsets of variables; Feature selection; Targeted clustering; Mixtures of numeric and categorical variables; Clustering in R; Multidimensional scaling; Proximities; Dissimilarities; Omics data (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00357-017-9240-z

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