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Graded LinClosure and Its Role in Relational Data Analysis

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Concept Lattices and Their Applications (CLA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4923))

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Abstract

We present graded extension of the algorithm LinClosure. Graded LinClosure can be used to compute degrees of semantic entailment from sets of fuzzy attribute implications. It can also be used together with graded extension of Ganter’s NextClosure algorithm to compute non-redundant bases of data tables with fuzzy attributes. We present foundations, the algorithm, and illustrative examples.

Supported by grant No. 1ET101370417 of GA AV ČR, by grant No. 201/05/0079 of the Czech Science Foundation, and by institutional support, research plan MSM 6198959214.

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Authors and Affiliations

  1. Dept. Systems Science and Industrial Engineering T. J. Watson School of Engineering and Applied Science, Binghamton University–SUNY, PO Box 6000, Binghamton, NY 13902–6000, USA

    Radim Belohlavek & Vilem Vychodil

  2. Dept. Computer Science, Palacky University, Olomouc Tomkova 40, CZ-779 00, Olomouc, Czech Republic

    Radim Belohlavek & Vilem Vychodil

Authors
  1. Radim Belohlavek
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  2. Vilem Vychodil
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Editor information

Sadok Ben Yahia Engelbert Mephu Nguifo Radim Belohlavek

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Belohlavek, R., Vychodil, V. (2008). Graded LinClosure and Its Role in Relational Data Analysis. In: Yahia, S.B., Nguifo, E.M., Belohlavek, R. (eds) Concept Lattices and Their Applications. CLA 2006. Lecture Notes in Computer Science(), vol 4923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78921-5_9

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  • DOI: https://doi.org/10.1007/978-3-540-78921-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78920-8

  • Online ISBN: 978-3-540-78921-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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