RJ Clustering Algorithm

Clustering algorithm for high dimensional data. This algorithm is ideal for data where 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')".


Reference manual

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2.5.0 by Rachael Shudde, a month ago

Browse source code at https://github.com/cran/RJcluster

Authors: Rachael Shudde [aut, cre] , Shahina Rahman [aut] , Valen Johnson [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, matrixStats, infotheo, rlang, stats, graphics, profvis, mclust, foreach

Suggests testthat, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

See at CRAN