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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=kamila to link to this page.
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