Implements the Expectation Maximisation (EM)/(EM*) algorithm for clustering finite gaussian mixture models for
both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the
dataset as the mean of the Gaussian(s). This version implements the faster alternative EM* that avoids revisiting
data by leveraging the heap structure. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices
(multivariate data)/standard-deviation (for univariate datasets) and priors.
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.

Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering
finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly
selecting the samples from the dataset as the mean (meu) of the Gaussian(s). This version implements the faster alternative
EM* that avoids revisiting data by leveraging the heap structure. The algorithm returns a set of Gaussian
parameters-posterior probabilities, mean (meu), co-variance matrices (multivariate)/standard-deviation
(univariate) and priors. 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.

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 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.