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Natural Induction and Conceptual Clustering: A Review of Applications

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dc.contributor.author Michalski, Ryszard S.
dc.contributor.author Kaufman, Kenneth A.
dc.contributor.author Pietrzykowski, Jaroslaw
dc.contributor.author Wojtusiak, Janusz
dc.contributor.author Mitchell, Scott
dc.contributor.author Seeman, Doug
dc.date.accessioned 2006-11-03T18:17:35Z
dc.date.available 2006-11-03T18:17:35Z
dc.date.issued 2006-06 en_US
dc.identifier.citation Michalski, R. S., Kaufman, K., Pietrzykowski, J., Wojtusiak, J., Mitchell, S. and Seeman, W.D., "Natural Induction and Conceptual Clustering: A Review of Applications," Reports of the Machine Learning and Inference Laboratory, MLI 06-3, George Mason University, Fairfax, VA, June, 2006 (Updated: August 23, 2006). en_US
dc.identifier.uri https://hdl.handle.net/1920/1497
dc.description.abstract Natural induction and conceptual clustering are two methodologies pioneered by the GMU Machine Learning and Inference Laboratory for discovering conceptual relationships in data, and presenting them in the forms easy for people to interpret and understand. The first methodology is for supervised learning (learning from examples) and the second for unsupervised learning (clustering). Examples of their application to a wide range of practical domains are presented, including bioinformatics, medicine, agriculture, volcanology, demographics, intrusion detection and computer user modeling, manufacturing, civil engineering, optimization of functions of very large number of variables (100-1000), design of complex engineering systems, tax fraud detection, and musicology. Most of the results were obtained by applying our recent natural induction program, AQ21, which is downloadable from http://www.mli.gmu.edu/msoftware.html. To give the Reader a quick insight into differences between natural induction implemented in AQ21 and some well-known learning methods, such as those implemented in C4.5, RIPPER, and CN2, as well as between conceptual clustering and conventional clustering, Sections 15 and 16 describe results from applying all these methods to very simple, designed problems.
dc.description.sponsorship The Laboratory's research has been supported in part by the National Science Foundation under Grants No. IIS-9906858 and IIS-0097476, and in part by the UMBC/LUCITE #32 grant. In a few cases, presented results have been obtained under earlier funding from the National Science Foundation, the Office of Naval Research, or the Defense Advanced Research Projects Agency. en_US
dc.format.extent 3504 bytes
dc.format.extent 509454 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 06-3 en_US
dc.relation.ispartofseries MLI 06-3 en_US
dc.subject data mining en_US
dc.subject Machine learning en_US
dc.subject natural induction en_US
dc.subject cluster analysis en_US
dc.subject conceptual clustering en_US
dc.title Natural Induction and Conceptual Clustering: A Review of Applications en_US
dc.type Technical report en_US


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