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Description
  • 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
  • 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)
Title
  • Formal concept analysis with background knowledge: Attribute Priorities
  • Formal concept analysis with background knowledge: Attribute Priorities (en)
skos:prefLabel
  • Formal concept analysis with background knowledge: Attribute Priorities
  • Formal concept analysis with background knowledge: Attribute Priorities (en)
skos:notation
  • RIV/61989592:15310/09:00010275!RIV10-MSM-15310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6198959214)
http://linked.open...iv/cisloPeriodika
  • 4
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 315372
http://linked.open...ai/riv/idVysledku
  • RIV/61989592:15310/09:00010275
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • data analysis; formal concept analysis; background knowledge (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [0E4A0D19DBDF]
http://linked.open...i/riv/nazevZdroje
  • IEEE Transactions on Systems, Man, and Cybernetics, Part C
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 39
http://linked.open...iv/tvurceVysledku
  • Bělohlávek, Radim
  • Vychodil, Vilém
http://linked.open...n/vavai/riv/zamer
issn
  • 1094-6977
number of pages
http://localhost/t...ganizacniJednotka
  • 15310
is http://linked.open...avai/riv/vysledek of
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