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Autoclass: An automatic classification systemThe task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.
Document ID
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Stutz, John
(Research Inst. for Advanced Computer Science Moffett Field, CA., United States)
Cheeseman, Peter
(NASA Ames Research Center Moffett Field, CA, United States)
Hanson, Robin
(Sterling Software Moffett Field, CA., United States)
Date Acquired
September 6, 2013
Publication Date
December 1, 1991
Publication Information
Publication: NASA, Washington, Technology 2001: The Second National Technology Transfer Conference and Exposition, Volume 1
Subject Category
Accession Number
Distribution Limits
Work of the US Gov. Public Use Permitted.
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