https://github.com/zcebeci/odetector
Outlier Detection Using Cluster Analysis
https://github.com/zcebeci/odetector
anomaly-detection cluster-analysis clustering clustering-methods data datapreparation datapreprocessing exception-handling fcm fraud-detection fuzzy-clustering novelty-detection outlier-detection outlier-removal outliers partitioning pcm r surprise-exploration
Last synced: 4 months ago
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Outlier Detection Using Cluster Analysis
- Host: GitHub
- URL: https://github.com/zcebeci/odetector
- Owner: zcebeci
- Created: 2022-01-05T09:49:13.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-10-11T13:25:29.000Z (almost 3 years ago)
- Last Synced: 2025-05-03T15:18:38.976Z (5 months ago)
- Topics: anomaly-detection, cluster-analysis, clustering, clustering-methods, data, datapreparation, datapreprocessing, exception-handling, fcm, fraud-detection, fuzzy-clustering, novelty-detection, outlier-detection, outlier-removal, outliers, partitioning, pcm, r, surprise-exploration
- Language: R
- Homepage:
- Size: 558 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# odetector
Outlier Detection Using Partitioning Clustering Algorithms
## Install the package 'odetector'
You can download a stable version of the package `odetector` from CRAN cloud as follows:
```{r}
install.package("odetector", repos="https://cloud.r-project.org")
```
In order to install the most recent version of the package `odetector` from the GitHub repository you should first install the `devtools` package from CRAN into your local system. Then you can install the package `odetector` using `install_github` of `devtools` package as shown with the R code chunks below:
```{r}
if(!require(devtools)) {install.packages('devtools'); require(devtools)}
devtools::install_github("zcebeci/odetector")
```
If you would like to have a compiled version of the vignettes of the package try to install the package `odetector` using `install_github` with `build_vignettes` argument set to TRUE as shown below:
```{r}
if(!require(devtools)) {install.packages('devtools'); require(devtools)}
devtools::install_github("zcebeci/odetector", build_vignettes=TRUE)
```
If you have not already installed `rmarkdown` and `prettydoc` in your local system, before running the above install commands firstly install these packages as following:
```{r}
install.packages('prettydoc')
```
## Load the package into R working space
After you installed the package `odetector`, run the following command in order to load it to R working space.
```{r}
library(odetector)
```
## Help for the package
To get help about the use of functions included in the package `odetector`, run `help` in R as seen in the following code chunk.
```{r}
help(package="odetector")
```
For reaching the detailed vignette about the usage of package with examples, run the following command and then click *HTML* link on the accessed page in your web browser. Read the vignette and apply the examples.
```{r}
browseVignettes("odetector")
```
## Cite the package
To cite the package please use one of the following items fits to your references list:
Cebeci Z, Cebeci C, Tahtali Y, Bayyurt L. 2022. Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering. PeerJ Computer Science 8:e1060 https://doi.org/10.7717/peerj-cs.1060
or in BibTeX format as follows:
```
@misc{zcebeciodetector,
author = {Cebeci, Z. and Cebeci, C. and Tahtali, Y. and Bayyurt, L.},
title = {Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering},
year = {2022},
journal = {PeerJ Computer Science},
volume={8:e1060},
howpublished = {\url{https://doi.org/10.7717/peerj-cs.1060}},
}
```