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

TL;DR

rCOSA is an updated R package that implements advanced clustering techniques on subsets of attributes, providing improved usability and additional analysis tools for multivariate data clustering.

Contribution

The paper introduces an up-to-date, user-friendly R package for COSA, extending its functionalities with new clustering and visualization methods.

Findings

Enhanced software usability and installation

Integration of hierarchical and partitional clustering methods

Facilitates analysis of multivariate data with subset attribute clustering

Abstract

\texttt{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 thse 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 Moreover, the available software is out-of-date.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: \url{https://github.com/mkampert/rCOSA}