Clustering algorithms are designed to identify groups in data where the traditional emphasis has been on numeric data. In consequence, many existing algorithms are devoted to this kind of data even though a combination of numeric and categorical data is more common in most business applications. Recently, new algorithms for clustering mixed-type data have been proposed based on Huang’s k-prototypes algorithm. This paper describes the R package clustMixType which provides an implementation of k-prototypes in R.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-048.zip
gower, cluster, CluMix, flexclust, fpc, clustMD, kamila, clustMixType, klaR, wesanderson, clusteval
Cluster, Multivariate, Environmetrics, Graphics, MachineLearning, Robust
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For attribution, please cite this work as
Szepannek, "The R Journal: clustMixType: User-Friendly Clustering of Mixed-Type Data in R", The R Journal, 2018
BibTeX citation
@article{RJ-2018-048,
author = {Szepannek, Gero},
title = {The R Journal: clustMixType: User-Friendly Clustering of Mixed-Type Data in R},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-048},
doi = {10.32614/RJ-2018-048},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {200-208}
}