SmartSifter: Online Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms

Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <doi:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.

Version: 0.1.0
Depends: R (≥ 3.3.1)
Imports: mvtnorm, rootSolve
Suggests: testthat
Published: 2016-09-14
Author: Lizhen Nie
Maintainer: Lizhen Nie <nie_lizhen at yahoo.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: SmartSifter citation info
CRAN checks: SmartSifter results

Downloads:

Reference manual: SmartSifter.pdf
Package source: SmartSifter_0.1.0.tar.gz
Windows binaries: r-devel: SmartSifter_0.1.0.zip, r-devel-gcc8: SmartSifter_0.1.0.zip, r-release: SmartSifter_0.1.0.zip, r-oldrel: SmartSifter_0.1.0.zip
OS X binaries: r-release: SmartSifter_0.1.0.tgz, r-oldrel: SmartSifter_0.1.0.tgz

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