Description: Clustering algorithm for high dimensional data. This algorithm is ideal for data where n << p.
Imports: Rcpp (>= 1.0.2), matrixStats, infotheo, rlang, stats,
graphics, profvis, mclust, doParallel, foreach, parallel
Packaged: 2020-09-15 18:02:51 UTC; rachaelshudde

Packaged: 2021-03-01 19:45:33 UTC; rachaelshudde

Packaged: 2021-03-10 21:38:46 UTC; rachaelshudde

Description: 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')".
Packaged: 2021-04-06 20:08:35 UTC; rachaelshudde

Title: A Fast Clustering Algorithm for High Dimensional Data Based on
person(given = "Suhasini", family = "Subba Rao",
Description: 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")'.
graphics, profvis, mclust, foreach, utils
Packaged: 2021-07-07 20:39:17 UTC; rachaelshudde

Packaged: 2021-07-15 15:21:50 UTC; rachaelshudde

Packaged: 2021-07-21 18:54:21 UTC; rachaelshudde

Packaged: 2021-08-18 21:08:44 UTC; rachaelshudde

Packaged: 2021-08-24 16:39:24 UTC; rachaelshudde

Packaged: 2021-09-03 18:17:37 UTC; rachaelshudde

Packaged: 2022-02-07 20:16:42 UTC; rachaelshudde