Data Reduction with Rough Sets.

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dc.contributor.author Shen, Qiang
dc.contributor.author Jensen, Richard
dc.date.accessioned 2009-03-23T12:29:21Z
dc.date.available 2009-03-23T12:29:21Z
dc.date.issued 2008
dc.identifier.citation Shen , Q & Jensen , R 2008 , ' Data Reduction with Rough Sets. ' . in Encyclopedia of Data Warehousing and Mining - 2nd Edition . 2nd edn , IGI Global , pp. 556-560 . , 10.4018/978-1-60566-010-3.ch087 en
dc.identifier.isbn 1605660108
dc.identifier.other PURE: 77858
dc.identifier.other dspace: 2160/1930
dc.identifier.uri http://hdl.handle.net/2160/1930
dc.description R. Jensen, Q. Shen, Data Reduction with Rough Sets, In: Encyclopedia of Data Warehousing and Mining - 2nd Edition, Vol. II, 2008. en
dc.description.abstract Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques fall in to one of two categories: those that transform the underlying meaning of the data features and those that are semantics-preserving. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation function. The process aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In knowledge discovery, feature selection methods are particularly desirable as these facilitate the interpretability of the resulting knowledge. Rough set theory has been used as such a tool with much success, enabling the discovery of data dependencies and the reduction of the number of features contained in a dataset using the data alone, requiring no additional information. en
dc.format.extent 5 en
dc.language.iso eng
dc.publisher IGI Global
dc.relation.ispartof Encyclopedia of Data Warehousing and Mining - 2nd Edition en
dc.title Data Reduction with Rough Sets. en
dc.type Text en
dc.type.publicationtype Book chapter en
dc.identifier.doi http://dx.doi.org/10.4018/978-1-60566-010-3.ch087
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en


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