HighDimOut-package: Outlier Detection Algorithms for High-Dimensional Data

In HighDimOut: Outlier Detection Algorithms for High-Dimensional Data

Description

Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].

Details

Package:	HighDimOut
Type:	Package
Version:	1.0
Date:	2015-03-30
License:	MIT

Author(s)

Cheng Fan

Maintainer: Cheng Fan <[e-mail]>

References

Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek. Angle-based outlier detection in high-dimensional data. KDD 2008, 444-452.

Hans-Peter Kriegel, Peer Kroger, Erich Schubert, Arthur Zimek. Interpreting and Unifying Outlier Scores. SDM 2011, 13-24.

Hans-Peter Kriegel, Peer Kroger, Erich Schubert, Arthur Zimek. Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. PAKDD 2009, 831-838.

Aleksandar Lazarevic, Vipin Kumar. Feature bagging for outlier detection. KDD 2005, 157-166.

HighDimOut documentation built on May 2, 2019, 12:16 p.m.