Natural Induction and Conceptual Clustering: A Review of Applications

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dc.contributor.authorMichalski, Ryszard S.
dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorPietrzykowski, Jaroslaw
dc.contributor.authorWojtusiak, Janusz
dc.contributor.authorMitchell, Scott
dc.contributor.authorSeeman, Doug
dc.date.accessioned2006-11-03T18:17:35Z
dc.date.available2006-11-03T18:17:35Z
dc.date.issued2006-06
dc.description.abstractNatural 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.sponsorshipThe 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.
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dc.identifier.citationMichalski, 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).
dc.identifier.urihttps://hdl.handle.net/1920/1497
dc.language.isoen_US
dc.relation.ispartofseriesP 06-3
dc.relation.ispartofseriesMLI 06-3
dc.subjectData mining
dc.subjectMachine learning
dc.subjectNatural induction
dc.subjectCluster analysis
dc.subjectConceptual clustering
dc.titleNatural Induction and Conceptual Clustering: A Review of Applications
dc.typeTechnical report

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