DCEM: Clustering Big Data using Expectation Maximization Star (EM*) Algorithm

Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure. The implementation supports both random and K-means++ based initialization. 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: 2.0.4
Depends: R (≥ 3.2.0)
Imports: mvtnorm (≥ 1.0.7), matrixcalc (≥ 1.0.3), MASS (≥ 7.3.49), Rcpp (≥ 1.0.2)
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
Published: 2020-08-02
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.com/parichit/DCEM/issues
License: GPL-3
URL: https://github.com/parichit/DCEM
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: DCEM results

Downloads:

Reference manual: DCEM.pdf
Vignettes: DCEM
Package source: DCEM_2.0.4.tar.gz
Windows binaries: r-devel: DCEM_2.0.4.zip, r-release: DCEM_2.0.2.zip, r-oldrel: DCEM_2.0.2.zip
macOS binaries: r-release: DCEM_2.0.2.tgz, r-oldrel: DCEM_2.0.4.tgz
Old sources: DCEM archive

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