Finite mixture model represents the presence of subpopulations within an overall population and describes the data in terms of mixture distribution. Finite mixture models are commonly used for model-based clustering, but they can be used also for other problems, like cluster-wise regression, mixture of generalized linear models and other mixtures. Finite mixture models for binary and categorical data are known under the name of latent class analysis.

Finite mixture model represents the presence of subpopulations within an overall population and describes the data in terms of mixture distribution. Finite mixture models are commonly used for model-based clustering, but they can be used also for other problems, like cluster-wise regression, mixture of generalized linear models and other mixtures. Finite mixture models for binary and categorical data are known under the name of latent class analysis (see ).

Finite mixture models are described in detail in:

McLachlan, G. and Peel, D. (2000). Finite Mixture Models. John Wiley & Sons.

and latent class analysis in:

McCutcheon, A.L. (1987). Latent Class Analysis. Sage.

Hagenaars J.A. & McCutcheon, A.L. (2009). Applied Latent Class Analysis. Cambridge University Press.