Package: DCEM 2.0.5

Sharma Parichit

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>.

Authors:Sharma Parichit [aut, cre, ctb], Kurban Hasan [aut, ctb], Dalkilic Mehmet [aut]

DCEM_2.0.5.tar.gz
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DCEM_2.0.5.tgz(r-4.4-x86_64)DCEM_2.0.5.tgz(r-4.4-arm64)DCEM_2.0.5.tgz(r-4.3-x86_64)DCEM_2.0.5.tgz(r-4.3-arm64)
DCEM_2.0.5.tar.gz(r-4.5-noble)DCEM_2.0.5.tar.gz(r-4.4-noble)
DCEM_2.0.5.tgz(r-4.4-emscripten)DCEM_2.0.5.tgz(r-4.3-emscripten)
DCEM.pdf |DCEM.html✨
DCEM/json (API)

# Install 'DCEM' in R:
install.packages('DCEM', repos = c('https://parichit.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/parichit/dcem/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

4.48 score 3 stars 7 scripts 260 downloads 2 mentions 6 exports 4 dependencies

Last updated 3 years agofrom:9c88ddaf03. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-win-x86_64NOTENov 15 2024
R-4.5-linux-x86_64NOTENov 15 2024
R-4.4-win-x86_64NOTENov 15 2024
R-4.4-mac-x86_64NOTENov 15 2024
R-4.4-mac-aarch64NOTENov 15 2024
R-4.3-win-x86_64NOTENov 15 2024
R-4.3-mac-x86_64NOTENov 15 2024
R-4.3-mac-aarch64NOTENov 15 2024

Exports:dcem_predictdcem_star_traindcem_testdcem_trainmeu_mvmeu_uv

Dependencies:MASSmatrixcalcmvtnormRcpp

DCEM

Rendered fromDCEM.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2022-01-15
Started: 2020-04-07

Readme and manuals

Help Manual

Help pageTopics
build_heap: Part of DCEM package.build_heap
DCEM: Clustering Big Data using Expectation Maximization Star (EM*) Algorithm.DCEM
dcem_cluster (multivariate data): Part of DCEM package.dcem_cluster_mv
dcem_cluster_uv (univariate data): Part of DCEM package.dcem_cluster_uv
dcem_predict: Part of DCEM package.dcem_predict
dcem_star_cluster_mv (multivariate data): Part of DCEM package.dcem_star_cluster_mv
dcem_star_cluster_uv (univariate data): Part of DCEM package.dcem_star_cluster_uv
dcem_star_train: Part of DCEM package.dcem_star_train
dcem_test: Part of DCEM package.dcem_test
dcem_train: Part of DCEM package.dcem_train
expectation_mv: Part of DCEM package.expectation_mv
expectation_uv: Part of DCEM package.expectation_uv
get_priors: Part of DCEM package.get_priors
insert_nodes: Part of DCEM package.insert_nodes
Ionosphere data: A dataset of 351 radar readingsionosphere_data
max_heapify: Part of DCEM package.max_heapify
maximisation_mv: Part of DCEM package.maximisation_mv
maximisation_uv: Part of DCEM package.maximisation_uv
meu_mv: Part of DCEM package.meu_mv
meu_mv_impr: Part of DCEM package.meu_mv_impr
meu_uv: Part of DCEM package.meu_uv
meu_uv_impr: Part of DCEM package.meu_uv_impr
separate_data: Part of DCEM package.separate_data
sigma_mv: Part of DCEM package.sigma_mv
sigma_uv: Part of DCEM package.sigma_uv
trim_data: Part of DCEM package. Used internally in the package.trim_data
update_weights: Part of DCEM package.update_weights
validate_data: Part of DCEM package. Used internally in the package.validate_data