Mason Archival Repository Service

Initial Considerations toward Knowledge Mining

Show simple item record

dc.contributor.author Kaufman, Kenneth A.
dc.contributor.author Michalski, Ryszard S.
dc.date.accessioned 2006-11-03T18:17:27Z
dc.date.available 2006-11-03T18:17:27Z
dc.date.issued 2004-10 en_US
dc.identifier.citation Kaufman, 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. en_US
dc.identifier.uri https://hdl.handle.net/1920/1488
dc.description.abstract In 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.sponsorship Support 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. en_US
dc.format.extent 3083 bytes
dc.format.extent 306048 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 04-6 en_US
dc.relation.ispartofseries MLI 04-4 en_US
dc.subject knowledge mining en_US
dc.subject data mining en_US
dc.subject inductive databases en_US
dc.subject Machine learning en_US
dc.title Initial Considerations toward Knowledge Mining en_US
dc.type Technical report en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search MARS


Browse

My Account

Statistics