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 expedites convergence via structure based data segregation.
The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma,
Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban,
Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.
| Version: |
2.0.5 |
| 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: |
2022-01-16 |
| DOI: |
10.32614/CRAN.package.DCEM |
| Author: |
Sharma Parichit [aut, cre, ctb],
Kurban Hasan [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 |
| Citation: |
DCEM citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
DCEM results |
Documentation:
Downloads:
Linking:
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