MATLAB Implementation of K-Means Clustering Algorithm

This program implements the K-Means clustering algorithm in MATLAB, structured into two primary components: the data management section and the algorithm implementation section. The data handling module is responsible for storing and preprocessing input datasets, while the K-Means algorithm module performs the actual clustering operations through iterative centroid updates. K-Means clustering is a widely-used unsupervised machine learning algorithm that partitions data points into distinct groups (clusters) based on feature similarity. The algorithm operates through an iterative process where cluster centroids are recursively updated by minimizing the sum of squared distances between data points and their assigned cluster centers, continuing until convergence criteria are met. The implementation includes detailed inline comments explaining key algorithmic steps such as: - Initial centroid selection using random sampling or k-means++ initialization - Euclidean distance calculation between data points and centroids - Cluster assignment based on minimum distance criteria - Centroid recalculation using mean position of assigned points - Convergence checking through centroid movement thresholds or maximum iterations This program enables efficient data clustering for various applications, providing clear visualization of the clustering process and resulting group separations. The well-documented code structure makes it suitable for both educational purposes and practical implementations in research or industrial applications. The modular design allows easy customization of distance metrics, initialization methods, and stopping criteria. Users can readily adapt the code for specific datasets or experimental requirements while maintaining algorithm integrity. We hope this implementation serves as a valuable resource for your data analysis tasks and facilitates deeper understanding of clustering methodologies in machine learning workflows.