A Fast Clustering Algorithm for High Dimensional Data Based on the Gram Matrix Decomposition

Clustering algorithm for high dimensional data. 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. To simulate test 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")'.


Reference manual

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3.1.0 by Rachael Shudde, 10 days ago

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

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

Documentation:   PDF Manual  

GPL (>= 2) license

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

Suggests testthat, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

See at CRAN