MATLAB Implementation of K-Means Clustering Algorithm

This article presents the source code implementation of K-means clustering algorithm in MATLAB, specifically designed for signal sorting applications. The implementation includes the core k-means clustering algorithm with Euclidean distance calculation and centroid updating mechanisms. The code features iterative optimization processes where cluster centroids are recalculated until convergence criteria are met. Key functions implemented include data point assignment to nearest clusters using vectorized operations and centroid recalculation based on mean values of assigned points. The algorithm handles signal feature extraction and grouping through distance-based similarity measures. Additional technical explanations have been incorporated to elaborate on k-means clustering concepts and their specific applications in signal sorting scenarios. These enhancements provide deeper insights into the mathematical foundations and practical implementation aspects, offering valuable information for researchers and practitioners working with signal processing and pattern recognition.