meu_mv_impr

<p>(matrix): The dataset provided by the user.</p>

data

num_meu

<p>Initialize the meu(s) by randomly selecting the samples from the dataset. It uses the proposed
implementation from K-means++: The Advantages of Careful Seeding, David Arthur and Sergei
Vassilvitskii. URL http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf.</p>

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>.

Sharma Parichit

meu_mv_impr: meu_mv_impr: Part of DCEM package.

Description

Usage

Arguments

Value