AUTOMATIC DATA CLASSIFICATION

The standard approach to classification in much of artificial intelligence
and statistical pattern recognition research involves partitioning of the
data into separate subsets, known classes. AUTOCLASS III, from NASA Ames
Research Center, uses the Bayesian approach in which classes are described
by probability distributions over the attributes of the objects, specified
by a model function and its parameters. The calculation of the probability
of each object's membership in each class provides a more intuitive
classification than absolute partitioning techniques.

AUTOCLASS III is applicable to most data sets consisting of independent
instances, each described by a fixed length vector of attribute values. An
attribute value may be a number, one of a set of attribute specific
symbols, or omitted. The user specifies a class probability distribution
function by associating attribute sets with supplied likelihood function
terms. AUTOCLASS then searches in the space of class numbers and
parameters for the maximally probable combination. It returns the set of
class probability function parameters, and the class membership
probabilities for each data instance.

AUTOCLASS III, ARC-13180, is written in Common Lisp, and is designed to be
platform independent. This program has been successfully run on Symbolics
and Explorer Lisp machines and has also been successfully used with the
following implementations of Common LISP on the Sun: Franz Allegro CL,
Lucid Common Lisp, and Austin Kyoto Common Lisp and similar UNIX
platforms; under the Lucid Common Lisp implementations on VAX/VMS v5.4,
VAX/Ultrix v4.1, and MIPS/Ultrix v4, rev. 179; and on the Macintosh
personal computer. The minimum Macintosh required is the IIci. An
electronic copy of the documentation is included on the distribution
medium. Program $900; documentation $21.

COSMIC
The University of Georgia
382 East Broad St, Athens, GA 30602-4272
706-542-3265,  FAX: 706-542-4807

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