Mean Shift Clustering Algorithm Implementation and Visualization

This repository provides the source code implementation for Mean Shift clustering, enabling cluster analysis through the mean shift algorithm with integrated visualization of results. The implementation employs kernel density estimation to identify cluster centroids by iteratively shifting points towards regions of higher density. Key parameters such as bandwidth (controlling the radius of the kernel window) and maximum iteration count can be adjusted to optimize clustering performance. The code structure includes functions for data preprocessing, centroid convergence calculation, and matplotlib-based visualization modules. Comparative analysis with other clustering algorithms (e.g., K-means, DBSCAN) can be conducted by modifying the evaluation metrics function. The algorithm's applicability can be tested across diverse datasets including Gaussian mixtures and real-world data through the dataset loader module. Overall, mean shift clustering serves as a robust non-parametric method with significant versatility for various domains including image segmentation and pattern recognition tasks.