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 (1971) <doi:10.2307/2528823>, Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>, Podani (1999) <doi:10.2307/1224438>, 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.

Package details

AuthorWeksi Budiaji
MaintainerWeksi Budiaji <[email protected]>
LicenseGPL-3
Version0.1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("kmed")

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kmed documentation built on Aug. 8, 2018, 9:03 a.m.