An approach to attribute generalization in incomplete information systems

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dc.contributor.author Li, Tianrui en_US
dc.contributor.author Ma, Jun en_US
dc.contributor.author Xu, Yang en_US
dc.contributor.author Yang, Ning en_US
dc.contributor.editor NA en_US
dc.date.accessioned 2010-06-16T05:00:05Z
dc.date.available 2010-06-16T05:00:05Z
dc.date.issued 2003 en_US
dc.identifier 2009005269 en_US
dc.identifier.citation Li Tianrui et al. 2003, 'An approach to attribute generalization in incomplete information systems', IEEE, Piscataway, USA, pp. 1698-1703. en_US
dc.identifier.issn 0-7803-8131-9 en_US
dc.identifier.other E1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/11927
dc.description.abstract Data mining is the efficient discovery of previously unknown patterns in large databases. How to use the existing knowledge to update knowledge is one of the important research areas in data mining. Since knowledge found by rough set is an apparent quantitative description and can be understood, of which data mining is in pursuit. Presently, several approaches based on classical rough set which aims at complete information system have been proposed for the mining task of updating knowledge. However, many information systems are incomplete in practical application. So it is important to develop approaches for updating knowledge in incomplete information systems in order to support more effective data mining. In this paper, based on an extension of the classical rough set theory for dealing with incomplete information systems, we have proposed a method for incremental updating approximations of a concept in incomplete information systems which may realize adding and deleting some attributes simultaneously at a time, that is very important to effectively handle dynamic attribute generalization and enhance the efficiency of data mining. en_US
dc.language en_US
dc.publisher IEEE en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/ICMLC.2003.1259770 en_US
dc.title An approach to attribute generalization in incomplete information systems en_US
dc.parent Proceedings of International Conference on Machine Learning and Cybernetics, 2003 en_US
dc.journal.volume 3 en_US
dc.journal.number en_US
dc.publocation Piscataway, USA en_US
dc.identifier.startpage 1698 en_US
dc.identifier.endpage 1703 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000030866 en_US
dc.personcode 999403 en_US
dc.personcode 0000030855 en_US
dc.personcode 0000030867 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Machine Learning and Cybernetics en_US
dc.date.activity 20031102 en_US
dc.location.activity Xian, China en_US
dc.description.keywords data mining , database management systems , rough set theory en_US


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