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.
| Version: | 0.1.1.1 |
| 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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