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

Documentation:

Reference manual:

kamila.pdf

Downloads:

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

Linking:

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