In data mining the goal is to develop methods for discovering previously unknown regularities from databases. The resulting models are interpreted and evaluated by domain experts, but some model evaluation criterion is needed also for the model construction process. The optimal choice would be to use the same criterion as the human experts, but this is usually impossible as the experts are not capable of expressing their evaluation criteria formally. On the other hand, it seems reasonable to assume that any model possessing the capability of maaking good predictions also captures some structure of the reality. For this reason, in predictive data mining the search for good models is guided by the expected predictive error of the models. In this paper we describe the Bayesian approach to predictive data mining in the finite mixture modeling framework. The finite mixture model family is a natural choice for domains where the data exhibits a clustering structure. In many real world domains this seems to be the case, as is demonstrated by our experimental results on a set of public domain databases.