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 |
| 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_2.0.4.zip, r-oldrel: DCEM_2.0.4.zip |
| macOS binaries: | r-release: DCEM_2.0.4.tgz, r-oldrel: DCEM_2.0.4.tgz |
| Old sources: | DCEM archive |
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