Implements the Expectation Maximisation (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 of the Gaussian(s). This version improves the parameter initialization on big datasets.
The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices
(multivariate data)/standard-deviation (for univariate datasets) and priors.
Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016)
Implements the Expectation Maximisation Algorithm for clustering the multivariate and univariate datasets. The package has been tested with numerical datasets (not recommended for categorical/ordinal data). The package comes bundled with a dataset for demostration (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: [email protected]]
For Reporting Issues
GitHub Repository Link
Installing from CRAN
Installing from the Binary Package
install.packages(dcem_1.0.0.tgz, repos = NULL, type="source")
How to use the package (An Example: working with the default bundled dataset)
The dcem package comes bundeled with the ionosphere_data.csv for demostration. 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 )
dcem_train()function), the data must be cleaned. This simply means to remove all redundant columns (example can be label colum). This datset 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)
dcem_cluster_uv()function for multivariate and univariate data respectively. These functions assign(s) the probabilistic weights to the sample(s) in the dataset.
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)
dcem_train()is stored in the dcem_out object. It contains the parameters associated with the clusters (Gaussian(s)). These parameters are namely - posterior probabilities, mean, co-variance (multivariate data) or standard-deviation (univariate data) and priors. Paste the following code in the R session to access any/all the output parameters.
 Posterior Probabilities: `**dcem_out$prob**`: A matrix of posterior-probabilities for the points in the dataset.  Mean(s): `**dcem_out$mean**` For multivariate data: It is a matrix of means for the gaussians. Each row in the matrix corresponds to a mean for the gaussian. For univariate data: It is a vector if means. Each element of the vector corresponds to one gaussian.  Co-variance matrices For multivariate data: `**dcem_out$cov**`: list of co-variance matrices for the gaussians. For univariate data: Standard-deviation `**dcem_out$sd**`: vector of standard deviation(s) for the gaussians.  Priors: `**dcem_out$prior**`: a vector of priors for the gaussians.
How to access the help (after installing the package)
?dcem_train() ?dcem_test() ?DCEM
This is the first stable realease of the DCEM package.
Support clustering of both multivariate and univariate data for finite gaussian misxture models.