Metadata-Version: 2.1
Name: predecon-exioreed
Version: 0.1
Summary: PreDeCon - An Implementation in Python, Compatible With Scikit-Learn
Home-page: https://github.com/exioReed/PreDeCon.git
Author: Maximilian Kaulmann
Author-email: exioreed@ownlink.eu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: joblib (>=0.14.0)
Requires-Dist: numpy (>=1.15.4)
Requires-Dist: scikit-learn (>=0.22.1)

# PreDeCon

This repository is not associated with the original authors of [Boehm,2004].

## About

_Subspace Preference Weighted Density Connected Clustering_ (PreDeCon) [Boehm,2004] can be seen as a
modification to the famous DBSCAN [Ester,1996] that addresses problems which arise in
high-dimensional spaces.

## Installation

Installation with `pip` from PyPI

```
$ pip install PreDeCon-exioreed
```

Alternatively, from source

```
$ pip install git+https://github.com/exioReed/PreDeCon@master#egg=PreDeCon-exioreed
```

or

```
$ git clone https://github.com/exioReed/PreDeCon.git
$ cd PreDeCon
$ pip install .
```

## References

`[Boehm,2004]` BÃ¶hm, C. et al., "Density Connected Clustering with Local Subspace Preferences".
In: _Proceedings of the 4th IEEE Internation Conference on Data Mining (ICDM)_,
Brighton, UK, 2004.

`[Ester,1996]` Ester, M. et al., "A Density-Based Algorithm for Discovering Clusters in Large
Spatial Databases with Noise".
In: _Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining_,
Portland, OR, 1996.

