kmed

Distance-Based k-Medoids

Algorithms of distance-based k-medoids clustering: simple and fast k-medoids (Park and Jun, 2009) <doi:10.1016/j.eswa.2008.01.039>, ranked k-medoids (Zadegan et al., 2013) <doi:10.1016/j.knosys.2012.10.012>, and step k-medoids (Yu et al., 2018) <doi:10.1016/j.eswa.2017.09.052>. Calculate distances for mixed variable data such as Gower, Podani, Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>, Huang (1997) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.9984&rep=rep1&type=pdf>, Harikumar and PV (2015) <doi:10.1016/j.procs.2015.10.077>, and Ahmad and Dey (2007) <doi:10.1016/j.datak.2007.03.016>. Cluster validation applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages.

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Description file content

Package
kmed
Type
Package
Title
Distance-Based k-Medoids
Version
0.2.0
Date
2019-01-02
Author
Weksi Budiaji
Maintainer
Weksi Budiaji
Description
Algorithms of distance-based k-medoids clustering: simple and fast k-medoids (Park and Jun, 2009) , ranked k-medoids (Zadegan et al., 2013) , and step k-medoids (Yu et al., 2018) . Calculate distances for mixed variable data such as Gower, Podani, Wishart (2003) , Huang (1997) , Harikumar and PV (2015) , and Ahmad and Dey (2007) . Cluster validation applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages.
Depends
R (>= 2.10)
License
GPL-3
LazyData
TRUE
RoxygenNote
6.1.0
Suggests
knitr, rmarkdown
VignetteBuilder
knitr
Imports
ggplot2
NeedsCompilation
no
Packaged
2019-01-02 17:32:58 UTC; Weksi
Repository
CRAN
Date/Publication
2019-01-02 18:30:02 UTC

install.packages('kmed')

0.2.0

a month ago

Weksi Budiaji

GPL-3

Depends on

R (>= 2.10)

Imports

ggplot2

Suggests

knitr, rmarkdown

Discussions