Gaussian Mixture Model with MDL-Based Cluster Determination

This is a Gaussian Mixture Model (GMM) developed in MATLAB environment for clustering analysis. The implementation employs the Expectation-Maximization (EM) algorithm for parameter estimation and utilizes the Minimum Description Length (MDL) criterion to automatically determine the optimal number of clusters. The package includes experimental datasets and represents code extracted from an international research paper. In the original publication, the authors provide comprehensive explanations of the model's theoretical foundations and experimental results, demonstrating its effectiveness and reliability. The code implementation features key functions for GMM initialization, parameter optimization, and cluster assignment, with detailed usage instructions and sample data to help researchers understand and apply the model efficiently. This open-source implementation enables researchers to quickly integrate Gaussian Mixture Models into their projects for advanced clustering analysis. The solution proves particularly valuable for data analysis and pattern recognition research, contributing to advancements in these fields through its robust algorithmic implementation and practical applicability.