  Mixture models extend the toolbox of clustering methods available to the data
analyst. They allow for an explicit definition of the cluster shapes and
structure within a probabilistic framework and exploit estimation and inference
techniques available for statistical models in general. In this chapter an
introduction to cluster analysis is provided, model-based clustering is related
to standard heuristic clustering methods and an overview on different ways to
specify the cluster model is given. Post-processing methods to determine a
suitable clustering, infer cluster distribution characteristics and validate
the cluster solution are discussed. The versatility of the model-based
clustering approach is illustrated by giving an overview on the different areas
of applications.
