Metadata-Version: 2.1
Name: predecon-exioreed
Version: 0.1.1
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
Description: # 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
        
        Install 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]` Boehm, 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.
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
