DCEM: Clustering for Multivariate and Univariate Data Using Expectation Maximization Algorithm

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.

Getting started

Package details

AuthorSharma Parichit [aut, cre, ctb], Kurban Hasan [aut, ctb], Jenne Mark [aut, ctb], Dalkilic Mehmet [aut]
MaintainerSharma Parichit <[email protected]>
URL https://github.iu.edu/parishar/DCEM
Package repositoryView on CRAN
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DCEM documentation built on Dec. 1, 2019, 1:18 a.m.