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

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”.

Version: 1.0
Depends: R (≥ 2.15.0)
Imports: mvtnorm, e1071, ggplot2, ellipse, doParallel, grid, foreach
Published: 2015-06-05
Author: Aishat Aloba, Kai Yu, Xin Dang, Yixin Chen, and Henry Bart Jr.
Maintainer: Aishat Aloba <adetokaloba at gmail.com>
License: GPL-3
NeedsCompilation: yes
CRAN checks: RobustEM results

Downloads:

Reference manual: RobustEM.pdf
Package source: RobustEM_1.0.tar.gz
Windows binaries: r-devel: RobustEM_1.0.zip, r-release: RobustEM_1.0.zip, r-oldrel: RobustEM_1.0.zip
OS X Mavericks binaries: r-release: RobustEM_1.0.tgz, r-oldrel: RobustEM_1.0.tgz

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