An implementation of the Fuzzy C-Means (FCM) clustering algorithm from scratch in Python. This project demonstrates the use of computational intelligence techniques for clustering numeric data and was developed as part of the Computational Intelligence course at Amirkabir University of Technology (AUT).

• Custom Implementation: Built from scratch without relying on external libraries for clustering logic.
• Fuzzy Membership Assignment: Each data point can belong to multiple clusters with varying degrees of membership.
• Dynamic Cluster Centers: Iterative updating of cluster centers based on fuzzy membership.
• Visualizations: Includes plots for clustered data and membership values.

• Programming Language: Python
• Libraries: NumPy, Matplotlib

This project explores fuzzy logic and clustering algorithms, providing a hands-on understanding of how fuzzy c-means operates and its applications in unsupervised learning.

This project was developed as part of the Computational Intelligence course at Amirkabir University of Technology (AUT).

This project is licensed under the MIT License. See the LICENSE file for details.