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
fitgmdist) or by specifying
parameter values (
gmdistribution). Then, use object
functions to perform cluster analysis (
mahal), evaluate the model
To learn about Gaussian mixture models, see Gaussian Mixture Models.
|Cumulative distribution function for Gaussian mixture distribution|
|Construct clusters from Gaussian mixture distribution|
|Mahalanobis distance to Gaussian mixture component|
|Probability density function for Gaussian mixture distribution|
|Posterior probability of Gaussian mixture component|
|Random variate from Gaussian mixture distribution|
Gaussian mixture models (GMMs) contain k multivariate normal density components, where k is a positive integer.
Partition data into clusters with different sizes and correlation structures.
Implement hard clustering on simulated data from a mixture of Gaussian distributions.
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.
Understand the basic types of cluster analysis.