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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

688

Version

0.2-10

License

Mozilla Public License 2.0

Issues

8

Pull Requests

0

Stars

17

Forks

0

Maintainer

Wei-Chen Chen

Last Published

February 1st, 2018

Functions in EMCluster (0.2-10)

Plot Multivariate Data

Plot Multivariate Data
Plot Projection and Contour

Plot Contour
MVN

Density of (Mixture) Multivariate Normal Distribution
Information Criteria

Information Criteria for Model-Based Clustering
Post I Information Functions

Post I Information Functions of EMCluster
Assign Class

Assign Class Id
LMT Functions

Likelihood Mixture Test (LMT) Functions of EMCluster
Conversion

Convert Matrices in Different Format
Print and Summary

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

Recolor Classification IDs
EM Algorithm

EM Algorithm for model-based clustering
Other Initializations

Other Initializations
Jaccard Index

Jaccard Index
Rand Index

Rand Index and Adjusted Rand Index
Likelihood Mixture Tests

Likelihood Mixture Tests
Dataset

Dataset for demonstrations
Single Step

Single E- and M-step
Initialization and EM

Initialization and EM Algorithm
All Internal Functions

All Internal Functions of EMCluster
EMCluster-package

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

EM Control Generator and Controller
Projection On 2D

Produce Projection on 2D
Plot EM Results

Plot Two Dimensional Data with clusters