K-means Clustering Algorithm

This high school project implements the K-means clustering algorithm in Python. K-means is a popular unsupervised learning algorithm used for clustering data points into K clusters based on their features.

How it Works

1. Initialization: K initial centroids are randomly selected from the data points.
2. Assignment: Each data point is assigned to the nearest centroid based on Euclidean distance.
3. Update: The centroids are recalculated as the mean of all data points assigned to each cluster.
4. Repeat: Steps 2 and 3 are repeated until convergence, i.e., until centroids no longer change.

Components

• assignPointsToCentroids: Function to assign data points to the nearest centroids based on Euclidean distance.
• recalcCentroids: Function to recalculate the centroids based on the assigned data points.
• KMeansND class: Class encapsulating the K-means algorithm with methods for computing centroids and providing a generator for animation.
• generateClusters: Function to generate random clusters of data points.
• initializeCentroids: Function to randomly initialize centroids from data points.
• animate: Function to draw each frame of the animation showing clustering process.
• setAxisLimits: Function to set axis limits for the plot to accommodate data points and centroids.

Dependencies

• Python 3.x
• NumPy
• Matplotlib

Usage

1. Run the main script to generate clusters and visualize the K-means clustering process:

python kmeans.py

2. Enter the desired number of clusters when prompted.

3. The script will generate random clusters of data points, perform K-means clustering, and animate the process.

Results

The animation demonstrates the K-means clustering algorithm in action, showing how centroids move iteratively to optimize cluster assignments.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.