Bayesian classification (AutoClass): theory and results

Abstract

We describe AutoClass, an approach to unsupervised classification based upon the classical mixture model, supplemented by a Bayesian method for determining the optimal classes. We include a moderately detailed exposition of the mathematics behind the AutoClass system. We emphasize that no current unsupervised classification system can produce maximally useful results when operated alone. It is the interaction between domain experts and the machine searching over the model space, that generates new knowledge. Both bring unique information and abilities to the database analysis task, and each enhances the others' effectiveness. We illustrate this point with several applications of AutoClass to complex real world databases, and describe the resulting successes and failures. 6.1 Introduction This chapter is a summary of our experience in using an automatic classification program (AutoClass) to extract useful information from databases. It also gives an outline of the principles that under...