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EMCluster

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  • Author: Wei-Chen Chen and Ranjan Maitra

EMCluster is an R package providing EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.

Installation

EMCluster requires

  • R version 3.0.0 or higher.
  • R package MASS, Matrix.

The package can be installed from the CRAN via the usual install.packages("EMCluster"), or via the devtools package:

library(devtools)
install_github("snoweye/EMCluster")

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Version

Install

install.packages('EMCluster')

Monthly Downloads

1,949

Version

0.2-12

License

Mozilla Public License 2.0

Issues

8

Pull Requests

0

Stars

17

Forks

0

Maintainer

Wei-Chen Chen

Last Published

March 22nd, 2019

Functions in EMCluster (0.2-12)

Plot EM Results

Plot Two Dimensional Data with clusters
Jaccard Index

Jaccard Index
Print and Summary

Functions for Printing or Summarizing Objects According to Classes
Recolor Classification IDs

Recolor Classification IDs
EM Control

EM Control Generator and Controller
Plot Multivariate Data

Plot Multivariate Data
All Internal Functions

All Internal Functions of EMCluster
MVN

Density of (Mixture) Multivariate Normal Distribution
Conversion

Convert Matrices in Different Format
Post I Information Functions

Post I Information Functions of EMCluster
Rand Index

Rand Index and Adjusted Rand Index
LMT Functions

Likelihood Mixture Test (LMT) Functions of EMCluster
Projection On 2D

Produce Projection on 2D
Plot Projection and Contour

Plot Contour
Dataset

Dataset for demonstrations
Initialization and EM

Initialization and EM Algorithm
Other Initializations

Other Initializations
EM Algorithm

EM Algorithm for model-based clustering
EMCluster-package

EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
Likelihood Mixture Tests

Likelihood Mixture Tests
Assign Class

Assign Class Id
Single Step

Single E- and M-step
Information Criteria

Information Criteria for Model-Based Clustering