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 |
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: kamila_0.1.2.tgz, r-oldrel: kamila_0.1.2.tgz |
Old sources: | kamila archive |
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