dcem_cluster_mv {DCEM}

dcem_cluster (multivariate data): Part of DCEM package.

Description

Implements the Expectation Maximization algorithm for multivariate data. This function is called by the dcem_train routine.

Usage

dcem_cluster_mv(data, meu, sigma, prior, num_clusters, iteration_count,
threshold, num_data)

Arguments

data

A matrix: The dataset provided by the user.

meu

(matrix): The matrix containing the initial meu(s).

sigma

(list): A list containing the initial covariance matrices.

prior

(vector): A vector containing the initial prior.

num_clusters

(numeric): The number of clusters specified by the user. Default value is 2.

iteration_count

(numeric): The number of iterations for which the algorithm should run, if the convergence is not achieved then the algorithm stops. Default: 200.

threshold

(numeric): A small value to check for convergence (if the estimated meu are within this specified threshold then the algorithm stops and exit).

Note: Choosing a very small value (0.0000001) for threshold can increase the runtime substantially and the algorithm may not converge. On the other hand, choosing a larger value (0.1) can lead to sub-optimal clustering. Default: 0.00001.

num_data

(numeric): The total number of observations in the data.

Value

A list of objects. This list contains parameters associated with the Gaussian(s) (posterior probabilities, meu, co-variance and prior)

1. (1) Posterior Probabilities: prob :A matrix of posterior-probabilities.

2. (2) Meu: meu: It is a matrix of meu(s). Each row in the matrix corresponds to one meu.

3. (3) Sigma: Co-variance matrices: sigma

4. (4) prior: prior: A vector of prior.

5. (5) Membership: membership: A vector of cluster membership for data.

References

Parichit Sharma, Hasan Kurban, Mehmet Dalkilic DCEM: An R package for clustering big data via data-centric modification of Expectation Maximization, SoftwareX, 17, 100944 URL https://doi.org/10.1016/j.softx.2021.100944