RobustEM: Robust Mixture Modeling Fitted via Spatial-EM Algorithm for Model-Based Clustering and Outlier Detection
Version 1.0

The Spatial-EM is a new robust EM algorithm for the finite mixture learning procedures. The algorithm utilizes median- based location and rank-based scatter estimators to replace sample mean and sample covariance matrix in each M step, hence enhancing stability and robustness of the algorithm. To understand more about this algorithm, read the article ''Yu, K., Dang, X., Bart Jr, H. and Chen, Y. (2015). Robust Model- based Learning via Spatial-EM Algorithm. IEEE Transactions on Knowledge and Data Engineering, 27(6), 1670-1682. doi:10.1109/TKDE.2014.2373355''.

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AuthorAishat Aloba, Kai Yu, Xin Dang, Yixin Chen, and Henry Bart Jr.
Date of publication2015-06-05 01:11:35
MaintainerAishat Aloba <adetokaloba@gmail.com>
LicenseGPL-3
Version1.0
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("RobustEM")

Man pages

cluster_em: Clustering Algorithm
cluster_em_outlier: Clustering and Outlier Detection Algorithm
cluster_plot: Clustering Plot
confusionMatrix: Confusion Matrix
errorRate: This function calculates the error rates for the different EM...
matchCluster: Correspondence between clusters
normalizedMI: Computes the normalized mutual information
trimmed_bic: Determines the number of components in the distribution.

Functions

cluster_em Man page
cluster_em_outlier Man page
cluster_plot Man page
confusionMatrix Man page
errorRate Man page
matchCluster Man page
normalizedMI Man page
trimmed_bic Man page

Files

src
src/wtdr_f.f
NAMESPACE
R
R/trimmed_bic.R
R/em_kotz.R
R/cluster_em_outlier.R
R/k_means.R
R/cluster_em.R
R/em_reg.R
R/conf_mat.R
R/em_rcm.R
R/clusterPlot.R
MD5
DESCRIPTION
man
man/matchCluster.Rd
man/cluster_em.Rd
man/normalizedMI.Rd
man/trimmed_bic.Rd
man/confusionMatrix.Rd
man/cluster_em_outlier.Rd
man/cluster_plot.Rd
man/errorRate.Rd
RobustEM documentation built on April 14, 2017, 10:05 a.m.