c(person(given = "Rachael",
family = "Shudde",
role = c("aut", "cre"),
email = "rachael.shudde@gmail.com"),
person(given = "Shahina",
family = "Rahman",
role = c("aut"),
email = "srahman@stat.tamu.edu"),
person(given = "Valen",
family = "Johnson",
role = c("aut"),
email = "vejohnson@exchange.tamu.edu"))
Clustering algorithm for high dimensional data. This algorithm is ideal for data where n << p.
Rcpp (>= 1.0.2), matrixStats, infotheo, rlang, stats,
graphics, profvis, mclust, doParallel, foreach, parallel
Rachael Shudde [aut, cre],
Shahina Rahman [aut],
Valen Johnson [aut]

Rcpp (>= 1.0.2), matrixStats, infotheo, rlang, stats,
graphics, profvis, mclust, foreach

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')".

A Fast Clustering Algorithm for High Dimensional Data Based on
the Gram Matrix Decomposition
c(person(given = "Shahina",
family = "Rahman",
role = c("aut"),
email = "srahman@stat.tamu.edu"),
person(given = "Valen E.",
family = "Johnson",
role = c("aut"),
email = "vejohnson@exchange.tamu.edu"),
person(given = "Suhasini", family = "Subba Rao",
role = c("aut"),
email = "suhasini@stat.tamu.edu"),
person(given = "Rachael",
family = "Shudde",
role = c("aut", "cre", "trl"),
email = "rachael.shudde@gmail.com"))
Shahina Rahman [aut],
Valen E. Johnson [aut],
Suhasini Subba Rao [aut],
Rachael Shudde [aut, cre, trl]
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")'.
Rcpp (>= 1.0.2), matrixStats, infotheo, rlang, stats,
graphics, profvis, mclust, foreach, utils