Package: GMKMcharlie
Type: Package
Title: Unsupervised Gaussian Mixture and Minkowski K-Means
Version: 1.0.3
Author: Charlie Wusuo Liu
Maintainer: Charlie Wusuo Liu <liuwusuo@gmail.com>
Description: High performance trainers for parameterizing and clustering weighted data. The Gaussian mixture (GM) module includes the conventional EM (expectation maximization) trainer, the component-wise EM trainer, the minimum-message-length EM trainer by Figueiredo and Jain (2002) <doi:10.1109/34.990138>. These trainers accept additional constraints on mixture weights and covariance eigen ratios. The K-means (KM) module offers clustering with the options of (i) deterministic and stochastic K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii) Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse representation of data input. The package improved the usual implementations of GM and KM training algorithms in various aspects. It is carefully crafted in multithreaded C++ for processing large data in industry use.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 1.0.0), RcppParallel
Suggests: MASS (>= 7.3.0), plot3D (>= 1.1.1)
LinkingTo: Rcpp, RcppParallel, RcppArmadillo
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2019-10-03 14:41:05 UTC; i56087
Repository: CRAN
Date/Publication: 2019-10-08 09:10:03 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 06:59:02 UTC; windows
Archs: i386, x64
