## Density-based Outlier Detection Algorithms

Versión 1.0.5 (10.2 KB) por
A MATLAB version of DDOutlier

642 descargas

De GitHub

## Story

An R package called DDOutlier  contains many density-based outlier detection algorithms. I find the package by accident in the searching for the sophisticated outlier detection methods. It proves the codes together with the associated papers, which are what I need. Then, I start to find a similar package in the MATLAB.

The MATLAB will never provide any algorithms that have not been proved stable and useful. It is an excellent advantage of the MATLAB. One will not worry that a function from MathWorks, Inc. has already been shown containing errors by other scientists. The MATLAB supports density-based methods from the bottom. It proves a function called ‘knnsearch’ and other associated functions.

## DDOutlier written in MATLAB

The MATLAB version of DDOutlier proves an interface to operate the neighbors or reverse neighbors of a data point. The neighborhood is the keystone of density-based outlier detection algorithms. In the meantime, the buffer in the DDOutlier package prevents frequently search the database. It is self-maintained. The user will not worry about them when operating the neighborhood.

## Supported algorithms

The MATLAB version directly supports two outlier detection algorithms:

1. Local Outlier Factor (LOF) in function LOFs.m, which is from .

2. Natural Outlier Factor (NOF) in function NOFs.m from  and .

Note that the R version of DDOutlier  supports many other algorithms.

## Functions in the package:

1. LRD.m : Local Reachability density .
2. NIS.m : Natural Influence Space .
3. NN.m : kth neighborhood .
4. NaNSearching.m : find the searching range when all the nature neighbors are found .
5. dataSet.m : store your data and buffer.
6. distance.m : calculate the distance of two data points if at least one of them considers another as friends.
7. kDistObj.m : generate a buffer for a specific searching range. Please use ‘clean all’ to clean it.
8. k_distance.m : calculate the k-distance .
9. matlabKNN.m : a function will generate the same output as KNN functions in R.
10. rNN.m : kth-reverse-neighborhood .
11. reach_distance.m : reachability distance .
12. rnbs.m : the times that one point is contained by the neighborhood of other points.

## Usage

A sample example can be found in tests.m. Remember to use ‘clean all’ to clean all the persist variables in the package. The package supports other distance metrics; however, only the euclidean metric is tested. So, it temporarily prevents outlier metric. The user is welcome to alter the code in dataSet.m for using other distance metrics.

## References

 Breunig, Markus M., et al. “LOF: identifying density-based local outliers.” ACM sigmod record. Vol. 29. No. 2. ACM, 2000.APA

 Huang, Jinlong, et al. “A non-parameter outlier detection algorithm based on Natural Neighbor.” Knowledge-Based Systems 92 (2016): 71-77.

 Zhu, Qingsheng, Ji Feng, and Jinlong Huang. “Natural neighbor: A self-adaptive neighborhood method without parameter K.” Pattern Recognition Letters 80 (2016): 30-36.APA

### Citar como

Blue Bird (2023). Density-based Outlier Detection Algorithms (https://github.com/BlueBirdHouse/DDoutlier), GitHub. Recuperado .

##### Compatibilidad con la versión de MATLAB
Se creó con R2019a
Compatible con cualquier versión
Windows macOS Linux
##### Categorías
Más información sobre Statistics and Machine Learning Toolbox en Help Center y MATLAB Answers.

### Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

#### +DDOutlier

No se pueden descargar versiones que utilicen la rama predeterminada de GitHub

Versión Publicado Notas de la versión
1.0.5

Automatically update.

1.0.4

New tags.

1.0.3

New title.

1.0.2

New title.

1.0.1

Renew Documents.

1.0.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.