Document: autoclass-kdd-95
Title: Bayesian Classification (AutoClass): Theory and Results
Author: Peter Cheeseman and John Stutz
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.
Section: Apps/Programming

Format: postscript
Files: /usr/share/doc/autoclass/kdd-95.ps.gz
