Researchers are often interested in identifying subgroups within their data to better understand heterogeneity within the population under study. This task has been the traditional domain of cluster analysis, but over the past decade or so finite mixture models have become an increasingly preferred alternative analytic technique. Sometimes referred to as “model-based clustering” the finite mixture model differs in important ways from more classical methods of cluster analysis, such as the K-Means algorithm. In this video, Dan provides an intuitive description of the underlying assumptions and purposes of the finite mixture model as contrasted with K-means clustering. He describes several important differences between finite mixture models and other cluster analysis techniques that might motivate applied researchers to select one approach over another. If you are interested in learning more about these techniques, including their implementation in popular software programs, you may wish to consider enrolling in our 5-day summer workshop on Cluster Analysis and Mixture Modeling.
Selecting the number of classes (or components) is one of the most challenging decisions to make when fitting a finite mixture model (including latent class analysis and latent profile analysis). In this post, we talk through the conventional wisdom on class enumeration, as well as when this breaks down.