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 objectgmdistribution
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
).
Functions
Topics
- 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
在模拟的数据实现软聚类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.