This web application demonstrates the K-means clustering algorithm, a fundamental technique in machine learning and data analysis. With an interactive and user-friendly interface, users can generate random data points, visualize them in 2D, and apply K-means clustering to group data points into clusters. The application provides insights into how K-means works, its real-world applications, and best practices for interpreting results.
- Generate Random Data: Create random data points in 2D and 3D for clustering.
- Interactive Visualization: Visualize data points, cluster centroids, and the clustering process.
- Real-world Applications: Explore the use cases of K-means in customer segmentation, genetic data analysis, document classification, and image compression.
- Easy-to-understand Tutorial: Learn about the algorithm's principles, its advantages, and how to interpret results.
- Best Practices: Discover best practices for data normalization, choosing the number of clusters, and interpreting clusters effectively.
- HTML, CSS, JavaScript for the web interface
- D3.js for data visualization
- Python (Flask) for server-side processing of K-means clustering
This project is licensed under the MIT License - see the MIT LICENSE file for details.