tryCatch_optimal_clust_GMM

<p>tryCatch function to prevent armadillo errors in GMM_arma_AIC_BIC</p>

internal

Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.

Lampros Mouselimis

ClusterR

Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids
and Affinity Propagation Clustering

Conrad Sanderson

Ryan Curtin

Siddharth Agrawal

Brendan Frey

Delbert Dueck

Vitalie Spinu

tryCatch_optimal_clust_GMM function

tryCatch function to prevent armadillo errors in GMM_arma_AIC_BIC — tryCatch_optimal_clust_GMM

tryCatch function to prevent armadillo errors in GMM_arma_AIC_BIC

tryCatch_optimal_clust_GMM: tryCatch function to prevent armadillo errors in GMM_arma_AIC_BIC

Description

Usage

Arguments