Package Overview

Implements the Expectation Maximisation Algorithm for clustering the multivariate and univariate datasets. There are two versions of EM implemented-EM* (converge faster by avoiding revisiting the data) and EM. For more details on EM*, see the 'References' section below.

The package has been tested with both real and simulated datasets. The package comes bundled with a dataset for demonstration (ionosphere_data.csv). More help about the package can be seen by typing ?DCEM in the R console (after installing the package).

Currently, data imputation is not supported and user has to handle the missing data before using the package.


For any Bug Fixes/Feature Update(s)

[Parichit Sharma:]

For Reporting Issues


Package Link on CRAN


Installation Instructions

Dependencies First, install all the required packages as follows:

install.packages(c("matrixcalc", "mvtnorm", "MASS", "Rcpp"))

Installing from CRAN


Installing from the Source Package

R CMD install DCEM_2.0.5.tar.gz

How to use the Package (Example: Working with the default bundled dataset)

# Example: Using the dcem_test()

# Load the library

# call the dcem_test() function and store the result in a variable
sample_out = dcem_test()

# Probe the returned values 
# Note: Detailed description of the returned values is also given in the section
# **_Displaying the output:_**

sample_out$prob         # estimated posterior probabilities
sample_out$meu          # estimated mean of the clusters
sample_out$sigma        # estimated covariance matrices
sample_out$priors       # estimated priors
sample_out$memebership  # membership of data points based on maximum liklihood (posterior probabilities)

An example of clustering the ionosphere data

ionosphere_data = read.csv2(
  file = paste(trimws(getwd()),"/data/","ionosphere_data.csv",sep = ""),
  sep = ",",
  header = FALSE,
  stringsAsFactors = FALSE

Paste the below code in the R session to clean the dataset.

ionosphere_data =  trim_data("35, 2", ionosphere_data)

Paste the below code in the R session to call the dcem_train() function.

dcem_out = dcem_train(data = ionosphere_data, threshold = 0.0001, iteration_count = 50, num_clusters = 2)
          [1] Posterior Probabilities: dcem_out$prob: A matrix of posterior-probabilities for the 
              points in the dataset.

          [2] Meu(s): dcem_out$meu

              For multivariate data: It is a matrix of meu(s). Each row in the  
              matrix corresponds to one meu.

              For univariate data: It is a vector if meu(s). Each element of the vector corresponds 
              to one meu.

          [3] Co-variance matrices 

              For multivariate data: dcem_out$sigma: List of co-variance matrices.

              For univariate data: dcem_out$sigma: Vector of standard deviation(s).

          [4] Priors: dcem_out$prior: A vector of prior.

          [5] Membership: dcem_out$membership: A vector of cluster membership for data.

How to access the help (after installing the package)


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DCEM documentation built on Jan. 16, 2022, 1:07 a.m.