MATLAB EM Algorithm Implementation for Data Clustering

This article presents a MATLAB-based implementation of the Expectation-Maximization (EM) algorithm designed for clustering applications. The implementation demonstrates notable computational efficiency, enabling accelerated cluster analysis workflows. The EM algorithm serves as a fundamental statistical method for partitioning data points into distinct clusters through iterative probability estimation. This MATLAB solution incorporates key algorithmic components including Gaussian mixture model initialization, expectation step (E-step) for posterior probability calculation, and maximization step (M-step) for parameter updates. The code structure facilitates easy adaptation for various dataset characteristics through configurable convergence thresholds and cluster parameters. By leveraging this implementation, researchers can effectively analyze data distributions, identify underlying patterns, and extract meaningful insights from complex datasets. The program's modular design allows for straightforward integration with preprocessing pipelines and visualization tools, making it suitable for both educational purposes and practical research applications.