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