MATLAB Implementation of K-Means Algorithm with Custom Code

This article discusses a custom MATLAB implementation of the k-means clustering algorithm. The implementation functions as a background program executed through function calls, deliberately avoiding MATLAB's native kmeans function. While this approach might seem unconventional to some users, it offers several significant advantages. Firstly, building the algorithm from scratch provides deeper insights into the clustering mechanism, including centroid initialization methods (typically using random selection or k-means++ improvement), iterative distance calculations (Euclidean distance computation between data points and centroids), and cluster reassignment processes. This hands-on approach enhances fundamental understanding of machine learning algorithms. Secondly, custom implementation allows for algorithm modifications and customization to meet specific project requirements, such as incorporating different distance metrics (Manhattan, cosine) or implementing alternative convergence criteria beyond standard centroid stability checks. The code structure typically involves main functions for data input, centroid initialization, iterative clustering loops, and result visualization. Finally, this methodology strengthens programming and algorithmic design capabilities, establishing a solid foundation for future research and professional work in data science and machine learning applications.