SmartSifter (0.1.0)

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Online Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms.

http://cran.r-project.org/web/packages/SmartSifter

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) . 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.

Maintainer: Lizhen Nie
Author(s): Lizhen Nie <nie_lizhen@yahoo.com>

License: GPL (>= 2)

Uses: mvtnorm, rootSolve, testthat

Released about 1 year ago.


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