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kmed (version 0.1.0)

Distance-Based k-Medoids

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 (1971) , Wishart (2003) , Podani (1999) , 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.

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Version

Install

install.packages('kmed')

Monthly Downloads

330

Version

0.1.0

License

GPL-3

Maintainer

Weksi Budiaji

Last Published

August 7th, 2018

Functions in kmed (0.1.0)

stepkmed

Step k-medoid algorithm from Yu et al.
fastkmed

Simple and fast k-medoid algorithm from Park and Jun.
consensusmatrix

Consensus matrix from A bootstrap replicate matrix
clustboot

Bootstrap replications for clustering alorithm
distmix

Distances for mixed variables.
rankkmed

Rank k-medoid algorithm from Zadegan et. al.
cooccur

Co-occurrence distance for binary/ categorical variables data.
matching

A pair distance for binary/ categorical variables.
distNumeric

A pair distance for continuous variables.
coocurance

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

Consensus matrix heatmap from A consensus matrix