Document: autoclass-results
Title: Bayesian Classification (AutoClass): Theory and Results
Author: John Stutz and Peter Cheeseman
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/Math

Format: PDF
Files: /usr/share/doc/autoclass/kdd-95.pdf
