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Gaussian mixture (GMM)

A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters.

Note
You could think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians.

Model

This algorithm represents a soft clustering model where each cluster is a Gaussian distribution with its own mean value and covariation matrix. Such a model can predict a cluster using the maximum likelihood principle.

It defines the labels by the following way:

KMeansModel mdl = trainer.fit(
    ignite,
    dataCache,
    vectorizer
);

double clusterLabel = mdl.predict(inputVector);

Trainer

GMM is a unsupervised learning algorithm. The GaussianMixture object implements the expectation-maximization (EM) algorithm for fitting mixture-of-Gaussian models. It can compute the Bayesian Information Criterion to assess the number of clusters in the data.

Presently, Ignite ML supports a few parameters for the GMM classification algorithm:

  • `maxCountOfClusters ` - the number of possible clusters

  • `maxCountOfIterations ` - one stop criteria (the other one is epsilon)

  • epsilon - delta of convergence(delta between old and new centroid’s values)

  • countOfComponents - the number of components

  • maxLikelihoodDivergence - maximum divergence between maximum of likelihood of vector in dataset and other for anomalies identification

  • minElementsForNewCluster - minimum required anomalies in terms of maxLikelihoodDivergence for creating new cluster

  • minClusterProbability - minimum cluster probability

// Set up the trainer
GmmTrainer trainer = new GmmTrainer(COUNT_OF_COMPONENTS);

// Build the model
GmmModel mdl = trainer
    .withMaxCountIterations(MAX_COUNT_ITERATIONS)
    .withMaxCountOfClusters(MAX_AMOUNT_OF_CLUSTERS)
    .fit(ignite, dataCache, vectorizer);

Example

To see how GMM clustering can be used in practice, try this example that is available on GitHub and delivered with every Apache Ignite distribution.