SmartSifter: Online Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Version 0.1.0

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.

Getting started

Package details

AuthorLizhen Nie <nie_lizhen@yahoo.com>
Date of publication2016-09-14 18:50:50
MaintainerLizhen Nie <nie_lizhen@yahoo.com>
LicenseGPL (>= 2)
Version0.1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("SmartSifter")

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SmartSifter documentation built on May 29, 2017, 9:12 p.m.