Description
A Fast Clustering Algorithm for High Dimensional Data Based on the Gram Matrix Decomposition.
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")'.
README.md
RJcluster
Maintained and written by: Rachael Shudde
Clustering algorithm for big data where the number of observations << the number of covariates. Implementation can be found here: https://arxiv.org/abs/1811.00956