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kmed (version 0.0.1)

Distance-Based k-Medoids

Description

A simple and fast distance-based k-medoids clustering algorithm from Park and Jun (2009) . Calculate distances for mixed variable data such as Gower (1971) , Wishart (2003) , Podani (1999) , Huang (1997) , and Harikumar and PV (2015) . 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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Version

Install

install.packages('kmed')

Monthly Downloads

330

Version

0.0.1

License

GPL-3

Maintainer

Weksi Budiaji

Last Published

February 12th, 2018

Functions in kmed (0.0.1)

distNumeric

A pair distance for continuous variables.
fastkmed

A simple and fast k-medoid algorithm from Park and Jun.
matching

A pair distance for binary/ categorical variables.
consensusmatrix

A consensus matrix from A bootstrap replicate matrix
coocurance

A co-occurrence distance for binary/ categorical variables data.
clustboot

A Bootstrap replications for clustering alorithm
clustheatmap

A consensus matrix heatmap from A consensus matrix
distmix

A distance for mixed variables.