Initial Considerations toward Knowledge Mining

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dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:27Z
dc.date.available2006-11-03T18:17:27Z
dc.date.issued2004-10
dc.description.abstractIn view of the tremendous production of computer data worldwide, there is a strong need for new powerful tools that can automatically generate useful knowledge from a variety of data, and present it in human-oriented forms. In efforts to satisfy this need, researchers have been exploring ideas and methods developed in machine learning, statistical data analysis, data mining, text mining, data visualization, pattern recognition, etc. The first part of this paper is a compendium of ideas on the applicability of symbolic machine learning and logical data analysis methods toward this goal. The second part outlines a multistrategy methodology for an emerging research direction, called knowledge mining, by which we mean the derivation of high-level concepts and descriptions from data through symbolic reasoning involving both data and relevant background knowledge. The effective use of background as well as previously created knowledge in reasoning about new data makes it possible for the knowledge mining system to derive useful new knowledge not only from large amounts of data, but also from limited and weakly relevant data.
dc.description.sponsorshipSupport for the Laboratory's research related to the presented results has been provided in part by the National Science Foundation under Grants No. DMI-9496192, IRI-9020266, IIS-9906858 and IIS-0097476; in part by the UMBC/LUCITE #32 grant; in part by the Office of Naval Research under Grant No. N00014-91-J-1351; in part by the Defense Advanced Research Projects Agency under Grant No. N00014-91-J-1854 administered by the Office of Naval Research; and in part by the Defense Advanced Research Projects Agency under Grants No. F49620-92-J-0549 and F49620-95-1-0462 administered by the Air Force Office of Scientific Research.
dc.format.extent3083 bytes
dc.format.extent306048 bytes
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dc.format.mimetypeapplication/pdf
dc.identifier.citationKaufman, K. and Michalski, R. S., "Initial Considerations toward Knowledge Mining," Reports of the Machine Learning and Inference Laboratory, MLI 04-4, George Mason University, Fairfax, VA, October, 2004.
dc.identifier.urihttps://hdl.handle.net/1920/1488
dc.language.isoen_US
dc.relation.ispartofseriesP 04-6
dc.relation.ispartofseriesMLI 04-4
dc.subjectKnowledge mining
dc.subjectData mining
dc.subjectInductive databases
dc.subjectMachine learning
dc.titleInitial Considerations toward Knowledge Mining
dc.typeTechnical report

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