**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.**

**Contact**

*For any Bug Fixes/Feature Update(s)*

[Parichit Sharma: parishar@iu.edu]

*For Reporting Issues*

*GitHub Repository Link*

**Installation Instructions**

**Dependencies** First, install all the required packages as follows:

install.packages(c(“matrixcalc”, “mvtnorm”, “MASS”, “Rcpp”))

*Installing from CRAN*

`install.packages("DCEM"")`

*Installing from the Source Package*

`R CMD install DCEM_2.0.3.tar.gz`

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

For demonstration purpose, users can call the

`dcem_test()`

function from the R console. This function invokes the dcem_star_train() on the bundled`ionosphere_data`

. Alternatively, a minimal quick start example is given below that explain how to cluster the`ionosphere_data`

from scratch.The DCEM package comes bundled with the ionosphere_data.csv for demonstration. Help about the dataset can be seen by typing

`?ionosphere_data`

in the R console. Additional details can be seen at the link Ionosphere data.To use this dataset, paste the following code into the R console.

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

: Before the model can be trained (*Cleaning the data*`dcem_train()`

function), the data must be cleaned. This simply means to remove all redundant columns (example can be label column). This dataset contains labels in the last column (35th) and only 0’s in the 2nd column so let’s remove them,

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

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

The dcem_train() learns the parameters of the Gaussian(s) from the input data.*Clustering the 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)`

The list returned by the*Displaying the output:*`dcem_train()`

is stored in theobject. It contains the parameters associated with the clusters (Gaussian(s)). These parameters are namely - posterior probabilities, meu, sigma and priors. Paste the following code in the R session to access any/all the output parameters.*dcem_out*

```
[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)*

```
?DCEM
?dcem_test
?dcem_star_train
?dcem_train
```