IBM® SPSS® Amos™ 28

One byproduct of the Bayesian approach to mixture modeling, as implemented in Amos, is the probabilistic assignment of individual cases to groups. Mixture modeling can thus be viewed as a form of clustering (Fraley & Raftery, 2002). As such, mixture modeling offers a model-based alternative to heuristic clustering methods such as k-means clustering.

In the Amos implementation, it is possible to assign some cases to groups in advance of the mixture modeling analysis. These cases provide a training set that assists in classifying the remaining cases. When used in this way, mixture modeling offers a model-based alternative to discriminant analysis.

The first example of mixture modeling (Example 34) in the User's Guide employs a dataset in which some cases are already classified. The mixture modeling analysis consists of classifying the remaining cases. Persons who have carried out multiple-group analyses using previous versions of Amos will find that practically no new learning is required for Example 34. In Amos, a mixture modeling analysis in which some cases are already classified is set up in almost the same way as an ordinary multiple-group analysis in which the group membership of every case is known in advance.