Algorithms for detection of outliers based on frequent pattern mining. Such algorithms follow the paradigm: if an instance contains more frequent patterns, it means that this data instance is unlikely to be an anomaly (He Zengyou, Xu Xiaofei, Huang Zhexue Joshua, Deng Shengchun (2005) <doi:10.2298/CSIS0501103H>). The package implements a list of existing state of the art algorithms as well as other published approaches: FPI, WFPI, FPOF, FPCOF, LFPOF, MFPOF, WCFPOF and WFPOF.
Version: | 0.1.0 |
Depends: | R (≥ 3.3.0) |
Imports: | pmml, XML, Matrix, R.utils, arules (≥ 1.5-4), foreach, doParallel, parallel, methods, pryr |
Suggests: | testthat |
Published: | 2017-11-22 |
Author: | Jaroslav Kuchar [aut, cre] |
Maintainer: | Jaroslav Kuchar <jaroslav.kuchar at gmail.com> |
BugReports: | https://github.com/jaroslav-kuchar/fpmoutliers/issues |
License: | Apache License (== 2.0) | file LICENSE |
URL: | https://github.com/jaroslav-kuchar/fpmoutliers |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | fpmoutliers results |
Reference manual: | fpmoutliers.pdf |
Package source: | fpmoutliers_0.1.0.tar.gz |
Windows binaries: | r-devel: fpmoutliers_0.1.0.zip, r-release: fpmoutliers_0.1.0.zip, r-oldrel: fpmoutliers_0.1.0.zip |
OS X El Capitan binaries: | r-release: fpmoutliers_0.1.0.tgz |
OS X Mavericks binaries: | r-oldrel: fpmoutliers_0.1.0.tgz |
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