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.
|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.|
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);
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);
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.
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