odetector
1.0.1Outlier Detection Using Partitioning Clustering Algorithms
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- References docs
- 38%
Downloads
Repository
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Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.0.1Latest
- 1.0.02022-10-04
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-10-04
- Total releases
- 2 / 4 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.0.0
- Bundled data
- 3.1 KB / 2 files
- Download size
- 191 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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