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 |
| 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:
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