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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.6 |
rolling linux/jammy R-4.5 | DCEM_2.0.6.tar.gz |
175.8 KiB |
2.0.6 |
rolling linux/noble R-4.5 | DCEM_2.0.6.tar.gz |
176.3 KiB |
2.0.6 |
rolling source/ R- | DCEM_2.0.6.tar.gz |
62.3 KiB |
2.0.6 |
latest linux/jammy R-4.5 | DCEM_2.0.6.tar.gz |
175.8 KiB |
2.0.6 |
latest linux/noble R-4.5 | DCEM_2.0.6.tar.gz |
176.3 KiB |
2.0.6 |
latest source/ R- | DCEM_2.0.6.tar.gz |
62.3 KiB |
2.0.6 |
2026-04-26 source/ R- | DCEM_2.0.6.tar.gz |
62.3 KiB |
2.0.6 |
2026-04-23 source/ R- | DCEM_2.0.6.tar.gz |
62.3 KiB |
2.0.5 |
2025-04-20 source/ R- | DCEM_2.0.5.tar.gz |
61.6 KiB |