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.

Depends: R (≥ 3.0.0)
Imports: stats, abind, KernSmooth, gtools, Rcpp, mclust, plyr
LinkingTo: Rcpp
Suggests: testthat, clustMD, ggplot2, Hmisc
Published: 2016-08-19
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
URL: https://github.com/ahfoss/kamila
NeedsCompilation: yes
Materials: README
CRAN checks: kamila results


Reference manual: kamila.pdf
Package source: kamila_0.1.1.1.tar.gz
Windows binaries: r-devel: kamila_0.1.1.1.zip, r-release: kamila_0.1.1.1.zip, r-oldrel: kamila_0.1.1.1.zip
OS X Mavericks binaries: r-release: kamila_0.1.1.1.tgz, r-oldrel: kamila_0.1.1.1.tgz


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