kamila: Methods for Clustering Mixed-Type Data

Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou (2018) <doi:10.18637/jss.v083.i13>.

Version: 0.1.2
Depends: R (≥ 3.0.0)
Imports: stats, abind, KernSmooth, gtools, Rcpp, plyr
LinkingTo: Rcpp
Suggests: testthat, clustMD, ggplot2, Hmisc
Published: 2020-03-13
DOI: 10.32614/CRAN.package.kamila
Author: Alexander Foss [aut, cre], Marianthi Markatou [aut]
Maintainer: Alexander Foss <alexanderhfoss at gmail.com>
BugReports: https://github.com/ahfoss/kamila/issues
License: GPL-3 | file LICENSE
URL: https://github.com/ahfoss/kamila
NeedsCompilation: yes
Citation: kamila citation info
Materials: README
CRAN checks: kamila results


Reference manual: kamila.pdf


Package source: kamila_0.1.2.tar.gz
Windows binaries: r-devel: kamila_0.1.2.zip, r-release: kamila_0.1.2.zip, r-oldrel: kamila_0.1.2.zip
macOS binaries: r-release (arm64): kamila_0.1.2.tgz, r-oldrel (arm64): kamila_0.1.2.tgz, r-release (x86_64): kamila_0.1.2.tgz, r-oldrel (x86_64): kamila_0.1.2.tgz
Old sources: kamila archive


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