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