AAAI Publications, Twenty-Second International FLAIRS Conference

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Mining Default Rules from Statistical Data
Gabriele Kern-Isberner, Matthias Thimm, Marc Finthammer, Jens Fisseler

Last modified: 2009-03-18


In this paper, we are interested in the qualitative knowledge that underlies some given probabilistic information. To represent such qualitative structures, we use ordinal conditional functions, OCFs, (or ranking functions) as a qualitative abstraction of probability functions. The basic idea for transforming probabilities into ordinal rankings is to find well-behaved clusterings of the negative logarithms of the probabilities. We show how popular clustering tools can be used for this, and propose measures for the evaluation of the clustering results in this context. From the so obtained ranking functions, we extract conditionals that may serve as a base for inductive default reasoning.


Data Mining; Ranking Functions; c-Representations; Default Logic

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