Publikationsdatum:
2013-08-31
Beschreibung:
The 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.
Schlagwort(e):
CYBERNETICS
Materialart:
NASA, Washington, Technology 2001: The Second National Technology Transfer Conference and Exposition, Volume 1; p 442-450
Format:
application/pdf
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