CMeans_fuzzy
Classification based on Fuzzy Logic(C-Means).
Table of Contents
About The Project
Computational Intelligence Course 2nd Project: In this project the Fuzzy
version of K-Means
algorithm is implemented. Each datapoint isn't forced to belong only to a specific cluster, but can belong to clusters to Verying Degrees
. Thats difference between Fuzzy Clustering
and Normal Clustering
.
Fuzzy CMeans Algorithm:
- Determine the number of clusters, then Generate the centroid of clusters randomly.
- Finding each data point belongs to which cluster (or clusters).
- Updating new centroid of clusters based on datapoints in the cluster.
- Repeat steps 2 and 3 until each cluster reaches stability.
Built With
Getting Started
Installation
- Clone the repository
git clone https://github.com/arminZolfaghari/CMeans_fuzzy.git
- Run CMeans.py with
python CMeans.py
Sample Of Output
Normal Clustering
Fuzzy Clustering
Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repository and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE
for more information.
Contact
Armin Zolfaghari Daryani - [email protected]
Project Link: https://github.com/arminZolfaghari/CMeans_fuzzy