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