MATLAB Implementation of EM Algorithm for Data Clustering

This content discusses the MATLAB implementation of the EM algorithm, specifically designed for data clustering applications. The EM (Expectation-Maximization) algorithm is a powerful unsupervised learning technique that iteratively estimates optimal model parameters through alternating E-steps and M-steps. The implementation typically involves specifying probability distributions for variables (commonly Gaussian mixtures for clustering) and iteratively refining parameter estimates until convergence. The MATLAB code provided here implements key algorithmic components including: initialization of cluster parameters, expectation step calculations for probabilistic assignments, and maximization step updates for distribution parameters. The code structure handles multiple iterations with convergence checking, typically using log-likelihood improvement thresholds. This implementation supports customizable distribution assumptions and includes visualization capabilities for cluster analysis results. When utilizing this EM algorithm implementation, users need to predefine the distribution type for variables (e.g., Gaussian mixtures for continuous data) before employing the algorithm to estimate corresponding parameters. The iterative nature of EM algorithm generally requires multiple cycles to achieve optimal solutions, with the provided MATLAB code offering configurable iteration limits and convergence criteria for practical data clustering analysis.