Package: odetector 1.0.0
Zeynel Cebeci
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.
Authors:
odetector_1.0.0.tar.gz
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odetector_1.0.0.tar.gz(r-4.5-noble)odetector_1.0.0.tar.gz(r-4.4-noble)
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odetector.pdf |odetector.html✨
odetector/json (API)
NEWS
| # Install 'odetector' in R: |
| install.packages('odetector', repos = c('https://zcebeci.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zcebeci/odetector/issues
- x3p4c - Synthetic data set consists of three variables with four clusters
anomaly-detectioncluster-analysisclusteringclustering-methodsdatadatapreparationdatapreprocessingexception-handlingfcmfraud-detectionfuzzy-clusteringnovelty-detectionoutlier-detectionoutlier-removaloutlierspartitioningpcmsurprise-exploration
Last updated 2 years agofrom:745aec1790. Checks:9 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 09 2025 |
| R-4.5-win | OK | Mar 09 2025 |
| R-4.5-mac | OK | Mar 09 2025 |
| R-4.5-linux | OK | Mar 09 2025 |
| R-4.4-win | OK | Mar 09 2025 |
| R-4.4-mac | OK | Mar 09 2025 |
| R-4.4-linux | OK | Mar 09 2025 |
| R-4.3-win | OK | Mar 09 2025 |
| R-4.3-mac | OK | Mar 09 2025 |
Exports:detect.outlierspairs.outliersplot.outliersprint.outliersremove.outlierssummary.outliers
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Outlier Detection Using Fuzzy and Possibilistic Clustering Algorithms | odetector-package |
| Detect outliers using typicality degrees | detect.outliers |
| Scatter plots for diagnosing outliers | pairs.outliers |
| Plot outliers | plot.outliers |
| Print outliers | print.outliers |
| Remove outliers | remove.outliers |
| Summary of outliers | summary.outliers |
| Synthetic data set consists of three variables with four clusters | x3p4c |