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AutoClass: A Bayesian Approach to ClassificationWe describe a Bayesian approach to the untutored discovery of classes in a set of cases, sometimes called finite mixture separation or clustering. The main difference between clustering and our approach is that we search for the "best" set of class descriptions rather than grouping the cases themselves. We describe our classes in terms of a probability distribution or density function, and the locally maximal posterior probability valued function parameters. We rate our classifications with an approximate joint probability of the data and functional form, marginalizing over the parameters. Approximation is necessitated by the computational complexity of the joint probability. Thus, we marginalize w.r.t. local maxima in the parameter space. We discuss the rationale behind our approach to classification. We give the mathematical development for the basic mixture model and describe the approximations needed for computational tractability. We instantiate the basic model with the discrete Dirichlet distribution and multivariant Gaussian density likelihoods. Then we show some results for both constructed and actual data.
Document ID
20010122938
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Stutz, John
(NASA Ames Research Center Moffett Field, CA United States)
Cheeseman, Peter
(Research Inst. for Advanced Computer Science Moffett Field, CA United States)
Hanson, Robin
(RECOM Technologies, Inc. Moffett Field, CA United States)
Taylor, Will
(RECOM Technologies, Inc. Moffett Field, CA United States)
Lum, Henry, Jr.
Date Acquired
August 20, 2013
Publication Date
January 1, 1994
Subject Category
Statistics And Probability
Meeting Information
Meeting: Fourteenth International MaxEnt Workshop (MaxEnt-94)
Location: Cambridge
Country: United Kingdom
Start Date: August 1, 1994
End Date: August 5, 1994
Funding Number(s)
PROJECT: RTOP 233-01-02
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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