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) . 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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("SmartSifter")

0.1.0 by Lizhen Nie, 3 years ago


Browse source code at https://github.com/cran/SmartSifter


Authors: Lizhen Nie <[email protected]>


Documentation:   PDF Manual  


GPL (>= 2) license


Imports mvtnorm, rootSolve

Suggests testthat


See at CRAN