Metadata-Version: 2.1
Name: denmune
Version: 0.0.3.0
Summary: This is the package for DenMune Clustering Algorithm published in paper https://doi.org/10.1016/j.patcog.2020.107589
Home-page: https://github.com/pypa/sampleproject
Author: Mohamed Ali Abbas
Author-email: mohamed.alyabbas@outlook.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/pypa/sampleproject/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

DenMune a clustering algorithm that can find clusters of arbitrary size, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne. The algorithm relies on a single parameter K (the number of nearest neighbors). The results show the superiority of DenMune. Enjoy the simplicty but the power of DenMune.

How to install denMune
====
Simply install DenMune clustering algorithm using pip command from the official Python repository
<code>pip install denmune</code>

How to use  DenMune
====
after installing DenMune, you just need to import it 
<code>from denmune import DenMune</code>
please note that first denmune (the file) in small letters, while the other one(the class itself) has D and M in capital cas while other letters are small

How to run and test
======
Simply use our repo2docker offered by mybinder.org, which encapsulate the algorithm and all required data in one place and allow you to test over 11 examples. 

Need to test examples one by one, then here another option. Use colab offered by google to test each example seperately.

here is a list of Google CoLab URL to use

| Dataset | CoLab URL |
------------------------
| Aggregation dataset | https://colab.research.google.com/drive/1K-Uqp-fmETmic4VZoZvV5t5XgRTzf4KO?usp=sharing |
| Chameleon DS1 | https://colab.research.google.com/drive/1LixPie1pZdWHxF1CXJIlwh1uTq-4iFYp?usp=sharing |
| Chameleon DS2 | https://colab.research.google.com/drive/16Ve-1JJCgTQrX7ITJjDrSXWmwT9tG1AA?usp=sharing |
| Chameleon DS3 | https://colab.research.google.com/drive/1mU5tV1sYWJpxqwyG-uA0yHMPZW7AzNuc?usp=sharing |
| Chameleon DS4 | https://colab.research.google.com/drive/1bDlsp1lVTDDXrDM8uWvo0_UY6ek73vUu?usp=sharing |
| Compound dataset | https://colab.research.google.com/drive/1TOv1mCLvAN24qvkh1f9H-ZERDgfoSMP6?usp=sharing |
| Iris dataset | https://colab.research.google.com/drive/1nKql57Xh7xVVu6NpTbg3vRdRg42R7hjm?usp=sharing |
| Jain dataset | https://colab.research.google.com/drive/1QJxXoZtoaMi3gvagZ2FPUtri4qbXOGl9?usp=sharing |
| Mouse dataset | https://colab.research.google.com/drive/11IpU1yaVaCa4H-d9yuwkjzywBfEfQGIp?usp=sharing |
| Pathbased | https://colab.research.google.com/drive/17DofhHs5I2xyhnNPJ6RWETDf7Te71TKm?usp=sharing |
| Spiral |https://colab.research.google.com/drive/1yW0Y14AiQYM6g7X4bJmUb3x3nson7Xup?usp=sharing |





