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kamila

Methods for Clustering Mixed-Type Data

Installation

About

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.

Citation kamila citation info
github.com/ahfoss/kamila
Bug report File report

Key Metrics

Version 0.1.2
R ≥ 3.0.0
Published 2020-03-13 1646 days ago
Needs compilation? yes
License GPL-3
License File
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Maintainer

Maintainer

Alexander Foss

Authors

Alexander Foss

aut / cre

Marianthi Markatou

aut

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

kamila archive

Depends

R ≥ 3.0.0

Imports

stats
abind
KernSmooth
gtools
Rcpp
plyr

Suggests

testthat
clustMD
ggplot2
Hmisc

LinkingTo

Rcpp