Description Usage Arguments Value Author(s) References
View source: R/dcem_cluster_mv.R
Implements the Expectation Maximization algorithm for multivariate data. This function is called by the dcem_train routine.
1 2  dcem_cluster_mv(data, meu, sigma, prior, num_clusters, iteration_count,
threshold, num_data)

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 suboptimal clustering. Default: 0.00001. 
num_data 
(numeric): The total number of observations in the data. 
A list of objects. This list contains parameters associated with the Gaussian(s) (posterior probabilities, meu, covariance and prior)
(1) Posterior Probabilities: prob :A matrix of posteriorprobabilities.
(2) Meu: meu: It is a matrix of meu(s). Each row in the matrix corresponds to one meu.
(3) Sigma: Covariance matrices: sigma
(4) prior: prior: A vector of prior.
Parichit Sharma [email protected], Hasan Kurban, Mark Jenne, Mehmet Dalkilic
This work is partially supported by NCI Grant 1R01CA21346601.
Using data to build a better EM: EM* for big data.
Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <https://doi.org/10.1007/s4106001700621>.
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