kmodR: K-Means with Simultaneous Outlier Detection

An implementation of the 'k-means–' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means– : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, "Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.

Version:	0.1.0
Suggests:	testthat
Published:	2015-03-26
Author:	David Charles Howe [aut, cre]
Maintainer:	David Charles Howe <kmodR at edgecondition.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	kmodR results

Documentation:

Reference manual:

kmodR.pdf

Downloads:

Package source:	kmodR_0.1.0.tar.gz
Windows binaries:	r-devel: kmodR_0.1.0.zip, r-release: kmodR_0.1.0.zip, r-oldrel: kmodR_0.1.0.zip
macOS binaries:	r-release (arm64): kmodR_0.1.0.tgz, r-oldrel (arm64): kmodR_0.1.0.tgz, r-release (x86_64): kmodR_0.1.0.tgz, r-oldrel (x86_64): kmodR_0.1.0.tgz

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