Gaussian Mixture Model for Clustering
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Resource Overview
A ready-to-use Gaussian Mixture Model implementation for clustering tasks, featuring 3 practical examples with code-based demonstrations
Detailed Documentation
The Gaussian Mixture Model (GMM) is a probabilistic clustering algorithm that represents data as a combination of multiple Gaussian distributions. This implementation provides a production-ready solution with three illustrative examples demonstrating key functionalities. The code includes parameter initialization using Expectation-Maximization (EM) algorithm, covariance matrix configuration options (full, tied, diagonal, spherical), and model selection via Bayesian Information Criterion (BIC). Each example showcases different dataset characteristics and corresponding GMM configurations for optimal clustering performance.
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