Model-Based Clustering Algorithm Implementation
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Resource Overview
MATLAB implementation for model-based clustering algorithms that processes complete MB clustering on given datasets. This implementation supports four fundamental model configurations with unequal unknown priors, employing Bayesian Information Criterion (BIC) for optimal model selection. The algorithm evaluates multiple Gaussian mixture models through Expectation-Maximization (EM) iterations and returns the BESTMODEL corresponding to the highest BIC score.
Detailed Documentation
The MATLAB implementation of model-based clustering algorithms serves as a powerful tool for performing comprehensive model-based clustering on given datasets. It handles four fundamental cluster models (spherical, diagonal, ellipsoidal, and tied covariance structures) while managing unequal unknown prior probabilities. The core algorithm utilizes iterative Expectation-Maximization (EM) procedures for parameter estimation and calculates Bayesian Information Criterion (BIC) values to objectively determine the optimal clustering configuration. Key functions include Gaussian mixture model initialization, log-likelihood computation, and covariance matrix regularization. This implementation enables researchers to gain deeper insights into data structures and make more accurate analytical decisions through statistically validated model selection. The output provides cluster assignments, model parameters, and probability estimates for each data point belonging to different components.
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