VISUAL CLUSTERS: Teaching Tool To Show how K-Means works K-Means Clustering Visualization Tool This repository contains a Streamlit-based application designed for interactive visualization of the k-means clustering algorithm. The app, developed by P.V. Sundar Balakrishnan, allows users to understand the conceptual aspects of k-means clustering through a hands-on approach.
Features Interactive sliders to control the number of data points, cluster standard deviation, and the maximum number of iterations. Input fields for specifying initial centroids. Real-time visualization of the k-means clustering process. Displays each iteration of the k-means algorithm with cluster centers and data points. Provides a summary table of cluster means, frequencies, and other relevant metrics. Color-coded plots for easy understanding of cluster formation. How to Run the App Clone this repository to your local machine. Ensure you have Python installed, along with necessary libraries like Streamlit, NumPy, Pandas, Matplotlib, and scikit-learn. Navigate to the directory containing the app script (STREAMLITKM-APP.py). Run the app using the command: streamlit run STREAMLITKM-APP.py. Requirements For a complete list of required Python packages, refer to the requirements.txt file.
License This project is open source and available under the MIT License.
Author P.V. Sundar Balakrishnan