PSO Clustering App

This project is an interactive Streamlit application that applies Particle Swarm Optimization (PSO) as a clustering technique. Unlike traditional clustering methods (e.g., K-Means), PSO uses a nature-inspired optimization algorithm that simulates the social behavior of swarms (like birds or fish) to find optimal cluster centers.

? Key Features:

Clustering with PSO

Automatically groups data points into clusters based on their similarity.

Demonstrates how swarm intelligence can be used in unsupervised learning.

Interactive Visualizations

Scatter plots showing how data points are clustered.

Cluster centers updated dynamically during the optimization process.

Visual tracking of swarm movement and convergence.

User Controls

Select the number of clusters.

Adjust PSO parameters (e.g., swarm size, inertia weight, cognitive/social coefficients, number of iterations).

Upload custom datasets for clustering.