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An Adjustable Description Quality Measure for Pattern Discovery in Large Databases Using the AQ Methodology

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dc.contributor.author Kaufman, Kenneth A.
dc.contributor.author Michalski, Ryszard S.
dc.date.accessioned 2006-11-03T18:17:01Z
dc.date.available 2006-11-03T18:17:01Z
dc.date.issued 2000-03 en_US
dc.identifier 10.1023/A:1008787919756 en_US
dc.identifier.citation Kaufman, Kenneth A. and Ryszard S. Michalski. "An Adjustable Description Quality Measure for Pattern Discovery in Large Databases Using the AQ Methodology." Journal of Intelligent Information Systems 14:2 (March 2000), p. 199-216. en_US
dc.identifier.uri https://hdl.handle.net/1920/1463
dc.description.abstract In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no noise, then the primary conditions a hypothesis must satisfy are consistency and completeness with regard to the data. In real-world applications, however, data are often noisy, and the insistence on the full completeness and consistency of the hypothesis is no longer valid. In such situations, the problem is to determine a hypothesis that represents the best trade-off between completeness and consistency. This paper presents an approach to this problem in which a learner seeks rules optimizing a rule quality criterion that combines the rule coverage (a measure of completeness) and training accuracy (a measure of inconsistency). These factors are combined into a single rule quality measure through a lexicographical evaluation functional (LEF). The method has been implemented in the AQ18 learning system for natural induction and pattern discovery, and compared with several other methods. Experiments have shown that the proposed method can be easily tailored to different problems and can simulate different rule learners.by modifying the parameter of the rule quality criterion.
dc.format.extent 3008 bytes
dc.format.extent 104705 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P00-14 en_US
dc.subject Machine learning en_US
dc.subject data mining en_US
dc.subject natural induction en_US
dc.subject AQ learning en_US
dc.subject decision rules en_US
dc.title An Adjustable Description Quality Measure for Pattern Discovery in Large Databases Using the AQ Methodology en_US
dc.type Postprint en_US


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