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.

Documentation

Manual: kamila.pdf
Vignette: None available.

Maintainer: Alexander Foss <alexanderhfoss at gmail.com>

Author(s): Alexander Foss*, Marianthi Markatou*

Install package and any missing dependencies by running this line in your R console:

install.packages("kamila")

Depends R (>= 3.0.0)
Imports stats, abind, KernSmooth, gtools, Rcpp, mclust, plyr
Suggests testthat, clustMD, ggplot2, Hmisc
Enhances
Linking to Rcpp
Reverse
depends
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linking to

Package kamila
Materials
URL https://github.com/ahfoss/kamila
Task Views
Version 0.1.1.1
Published 2016-08-19
License GPL-3
BugReports https://github.com/ahfoss/kamila/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks kamila check results
Package source kamila_0.1.1.1.tar.gz