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 | fileLICENSE |
| URL: | https://github.com/ahfoss/kamila |
| NeedsCompilation: | yes |
| Citation: | kamila citation info |
| Materials: | README |
| CRAN checks: | kamila results |
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