An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers. See <https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf>.
|Author||Chris Fraley [aut, cre], Leland Wilkinson [ctb]|
|Date of publication||2016-12-24 11:23:58|
|Maintainer||Chris Fraley <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
dots: One dimensional dots dataset - outlier detection example
ex2D: Two dimensional dataset - outlier detection example
getHDmembers: Partitioning Stage of the HDoutliers Algorithm
getHDoutliers: Outlier Detection Stage of the HD Outliers Algorithm
HDoutliers: Leland Wilkinson's HDoutliers Algorithm for Outlier Detection
plotHDoutliers: Display Outlier Detection Results
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.