Implements the Expectation Maximization algorithm for the univariate data. This function is internally called by the dcem_train routine.
dcem_cluster_uv(data, mean_vector, sd_vector, prior_vec, num, iteration_count,
threshold, numrows, numcols)(matrix): The dataset provided by the user (converted to matrix format).
(vector): The vector containing the initial means of the Gaussians.
(vector): The vector containing the initial standard deviation for the Gaussians. The initial sd are set to be 1. They are updated during the iterations of the algorithm.
(vector): The vector containing the initial priors for the Gaussians. They are initialized uniformly.
(numeric): The number of clusters specified by the user. Default value is 2.
(numeric): The number of iterations for which the algorithm should run. if the convergence is not achieved within the specified threshold then the algorithm stops and exits. Default: 200.
(numeric): A small value to check for convergence (if the estimated mean(s) 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.
(numeric): Number of rows in the dataset (After processing the missing values).
(numeric): Number of columns in the dataset (After processing the missing values).
A list of objects. This list contains parameters associated with the Gaussian(s) (posterior probabilities, mean, co-variance/standard-deviation and priors)
(1) Posterior Probabilities: sample_out$prob A matrix of posterior-probabilities
(2) Mean(s): sample_out$mean
For univariate data: It is a vector of means. Each element of the vector corresponds to one Gaussian.
(3) Standard-deviation(s): sample_out$sd
For univariate data: Vector of standard deviation for the Gaussian(s))
(4) Priors: sample_out$prior A vector of priors for the Gaussian(s).
Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <https://doi.org/10.1007/s41060-017-0062-1>.