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Foundation ::
Artificial Intelligence and Expert Systems ::
AUTOCLASS III
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AUTOCLASS III
Automatic Class Discovery from Data
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Moderators:
Adopt This Application!
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SOURCE CODE AVAILABLE
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The program AUTOCLASS III, Automatic Class Discovery from Data, uses
Bayesian probability theory to provide a simple and extensible approach to
problems such as classification and general mixture separation. Its theoretical
basis is free from ad hoc quantities, and in particular free of
any measures which alter the data to suit the needs of the program. As a result,
the elementary classification model used lends itself easily to extensions.
The standard approach to classification in much of artificial intelligence
and statistical pattern recognition research involves partitioning
of the data into separate subsets, known as classes. AUTOCLASS III 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 carries the NASA case number ARC-13180. It was originally released as part of the NASA COSMIC collection.
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