RJcluster-package: RJ Clustering Algorithm

In rshudde/RJcluster: RJ Clustering Algorithm

Description

Clustering algorithm for high dimensional data. This algorithm is ideal for data where N = P or N < P. Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. When the P-dimensional feature vectors of objects are drawn independently from a K distinct mixture distribution, the N-dimensional rows of the modified Gram matrix XX^T/P converges almost surely to K distinct cluster means. This transformation/projection thus allows the clusters to be tighter with order of P. To simulate data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'.

Details

Package:	RJcluster
Type:	Package
Version:	2.5.0
Date:	03-31-2021
License:	GPL>=2

Author(s)

Shahina Rahman [aut], Valen E. Johnson [aut], Suhasini Subba Rao [aut], Rachael Shudde [aut, cre, trl]

Maintainer: Rachael Shudde <[e-mail]>

rshudde/RJcluster documentation built on April 26, 2021, 5:21 p.m.