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Gaussian Mixture Models

Cluster based on Gaussian mixture models using the Expectation-Maximization algorithm

Gaussian mixture models (GMMs) assign each observation to a cluster by maximizing the posterior probability that a data point belongs to its assigned cluster. Create a GMM object `gmdistribution` by fitting a model to data (`fitgmdist`) or by specifying parameter values (`gmdistribution`). Then, use object functions to perform cluster analysis (`cluster`, `posterior`, `mahal`), evaluate the model (`cdf`, `pdf`), and generate random variates (`random`).

To learn about Gaussian mixture models, see Gaussian Mixture Models.

Functions

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 `fitgmdist` Fit Gaussian mixture model to data `gmdistribution` Create Gaussian mixture model
 `cdf` Cumulative distribution function for Gaussian mixture distribution `cluster` Construct clusters from Gaussian mixture distribution `mahal` Mahalanobis distance to Gaussian mixture component `pdf` Probability density function for Gaussian mixture distribution `posterior` Posterior probability of Gaussian mixture component `random` Random variate from Gaussian mixture distribution

Topics

Gaussian Mixture Models

Gaussian mixture models (GMMs) contain k multivariate normal density components, where k is a positive integer.

Cluster Using Gaussian Mixture Models

Partition data into clusters with different sizes and correlation structures.

Cluster Gaussian Mixture Data Using Hard Clustering

Implement hard clustering on simulated data from a mixture of Gaussian distributions.

Cluster Gaussian Mixture Data Using Soft Clustering

Implement soft clustering on simulated data from a mixture of Gaussian distributions.

Tune Gaussian Mixture Models

Determine the best Gaussian mixture model (GMM) fit by adjusting the number of components and the component covariance matrix structure.

Introduction to Cluster Analysis

Understand the basic types of cluster analysis.