DCEM: 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). Future versions will improve the parameter initialization and execution 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) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by NCI Grant 1R01CA213466-01.

Version: 0.0.1
Depends: R (≥ 3.4.0)
Imports: mvtnorm (≥ 1.0.7), matrixcalc (≥ 1.0.3), MASS (≥ 7.3.49)
Published: 2018-09-30
Author: Sharma Parichit [aut, cre, ctb], Kurban Hasan [aut, ctb], Jenne Mark [aut, ctb], Dalkilic Mehmet [aut]
Maintainer: Sharma Parichit <parishar at iu.edu>
BugReports: https://github.iu.edu/parishar/DCEM/issues
License: GPL-3
URL: https://github.iu.edu/parishar/DCEM
NeedsCompilation: no
Materials: README NEWS
CRAN checks: DCEM results

Downloads:

Reference manual: DCEM.pdf
Package source: DCEM_0.0.1.tar.gz
Windows binaries: r-devel: DCEM_0.0.1.zip, r-release: DCEM_0.0.1.zip, r-oldrel: DCEM_0.0.1.zip
OS X binaries: r-release: DCEM_0.0.1.tgz, r-oldrel: DCEM_0.0.1.tgz

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