Unsupervised Gaussian Mixture and Minkowski K-Means

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) . 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.


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install.packages("GMKMcharlie")

1.0.3 by Charlie Wusuo Liu, 2 months ago


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


Authors: Charlie Wusuo Liu


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, RcppParallel

Suggests MASS, plot3D

Linking to Rcpp, RcppParallel, RcppArmadillo

System requirements: GNU make


See at CRAN