. . "Formal concept analysis with background knowledge: Attribute Priorities"@en . . "RIV/61989592:15310/09:00010275" . . "Formal concept analysis with background knowledge: Attribute Priorities" . . "[0E4A0D19DBDF]" . "This paper deals with background knowledge in knowledge extraction from binary data. A background knowledge represents an additional piece of information a user may have along with the input data. Such information can be considered as specifying the type of knowledge a user is looking for in the data. In particular, we emphasize the need for taking into account background knowledge in formal concept analysis. We present an approach to modeling background knowledge which represents user's priorities regarding attributes and their relative importance. Such priorities serve as a constraint---only those formal concepts which are compatible with user's priorities are considered relevant, extracted from data, and presented to the user. Our approach has two main practical features. First, the number of formal concepts presented to the user may get significantly reduced. As a result, the user is supplied with relevant formal concepts only and is not overwhelmed by a large number o"@en . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . "IEEE Transactions on Systems, Man, and Cybernetics, Part C" . . . "Formal concept analysis with background knowledge: Attribute Priorities"@en . "15310" . "data analysis; formal concept analysis; background knowledge"@en . "Z(MSM6198959214)" . . . "2"^^ . . . "39" . . "Vychodil, Vil\u00E9m" . . "315372" . "4" . . "This paper deals with background knowledge in knowledge extraction from binary data. A background knowledge represents an additional piece of information a user may have along with the input data. Such information can be considered as specifying the type of knowledge a user is looking for in the data. In particular, we emphasize the need for taking into account background knowledge in formal concept analysis. We present an approach to modeling background knowledge which represents user's priorities regarding attributes and their relative importance. Such priorities serve as a constraint---only those formal concepts which are compatible with user's priorities are considered relevant, extracted from data, and presented to the user. Our approach has two main practical features. First, the number of formal concepts presented to the user may get significantly reduced. As a result, the user is supplied with relevant formal concepts only and is not overwhelmed by a large number o" . "2"^^ . "11"^^ . "1094-6977" . . "RIV/61989592:15310/09:00010275!RIV10-MSM-15310___" . "Formal concept analysis with background knowledge: Attribute Priorities" . "B\u011Blohl\u00E1vek, Radim" . .