Glossary

Gaussian Mixture Model (GMM)

What is a Gaussian Mixture Model?

Mixture models are used to discover subpopulations, or clusters, within a set of data; a Gaussian mixture model has parameters that correspond to a probability that a specific data point belongs to a specific subpopulation. The probability function is a Gaussian distribution – the traditional bell-shaped curve with a mean and standard deviation – and can be used for single or multiple variable models.

Why are Gaussian Mixture Models Important?

Gaussian mixture models, typically deployed in unsupervised machine learning, are widely used in applications like financial investments and pricing, natural language analysis, image recognition, and predictive maintenance. They are widely available in open-source libraries, are easy to implement, and are faster and more stable than other solutions like gradient descent in converging to a minimum.

How C3 AI Enables Organizations to Use Gaussian Mixture Models

C3 AI makes it easy to apply Gaussian mixture models to address domain-specific AI applications to deliver business value today. The C3 AI® Suite is a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. C3 AI supports Gaussian mixture models to analyze data within an ML pipeline using either the low-code C3 AI ML Studio development environment or the no-code C3 AI Ex Machina tool. Within Ex Machina, for example, a business analyst can graphically link a data set to an analytics model to run a Gaussian mixture model to determine the optimal segmentation into clusters, and then apply that model to classify unlabeled incoming data, all without writing a line of code.