odetector: Outlier Detection Using Partitioning Clustering Algorithms

An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.

Version: 1.0.1
Depends: R (≥ 3.0.0)
Imports: ppclust, utils, graphics, grDevices
Suggests: knitr, rmarkdown, prettydoc
Published: 2022-11-08
Author: Zeynel Cebeci ORCID iD [aut, cre], Cagatay Cebeci ORCID iD [ctb], Yalcin Tahtali ORCID iD [ctb]
Maintainer: Zeynel Cebeci <zcebeci at cukurova.edu.tr>
BugReports: https://github.com/zcebeci/odetector/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/zcebeci/odetector
NeedsCompilation: no
Citation: odetector citation info
Materials: NEWS
CRAN checks: odetector results


Reference manual: odetector.pdf
Vignettes: Outlier Detection Using Possibilistic and Fuzzy Clustering Algorithms


Package source: odetector_1.0.1.tar.gz
Windows binaries: r-devel: odetector_1.0.1.zip, r-release: odetector_1.0.1.zip, r-oldrel: odetector_1.0.1.zip
macOS binaries: r-release (arm64): odetector_1.0.1.tgz, r-oldrel (arm64): odetector_1.0.1.tgz, r-release (x86_64): odetector_1.0.1.tgz, r-oldrel (x86_64): odetector_1.0.1.tgz
Old sources: odetector archive


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