CMeans_fuzzy

Classification based on Fuzzy Logic(C-Means).

Table of Contents

1. About The Project
   • Fuzzy CMeans Algorithm
   • Built With
2. Getting Started
   • Installation
3. Sample Of Output
   • Normal Clustering
   • Fuzzy Clustering
4. Contributing
5. License
6. Contact

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.

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Fuzzy CMeans Algorithm:

1. Determine the number of clusters, then Generate the centroid of clusters randomly.
2. Finding each data point belongs to which cluster (or clusters).
3. Updating new centroid of clusters based on datapoints in the cluster.
4. Repeat steps 2 and 3 until each cluster reaches stability.

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Built With

• python

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Getting Started

Installation

1. Clone the repository

   git clone https://github.com/arminZolfaghari/CMeans_fuzzy.git

2. Run CMeans.py with python CMeans.py

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Sample Of Output

Normal Clustering

Fuzzy Clustering

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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!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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Contact

Armin Zolfaghari Daryani - [email protected]
Project Link: https://github.com/arminZolfaghari/CMeans_fuzzy

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