DCEM (0.0.2)

Clustering for Multivariate and Univariate Data Using Expectation Maximization Algorithm.


Implements the Expectation Maximisation (EM) algorithm for clustering finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the dataset as the mean of the Gaussian(s). This version improves the parameter initialization on big datasets. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices (multivariate data)/standard-deviation (for univariate datasets) and priors. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) . This work is partially supported by NCI Grant 1R01CA213466-01.

Maintainer: Sharma Parichit
Author(s): Sharma Parichit [aut, cre, ctb], Kurban Hasan [aut, ctb], Jenne Mark [aut, ctb], Dalkilic Mehmet [aut]

License: GPL-3

Uses: MASS, matrixcalc, mvtnorm

Released about 1 month ago.