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Fuzzy functional dependencies and linguistic interpretations employed in knowledge discovery tasks from relational databases

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F20%3A10244402" target="_blank" >RIV/61989100:27510/20:10244402 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S0952197619303136" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197619303136</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.engappai.2019.103395" target="_blank" >10.1016/j.engappai.2019.103395</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Fuzzy functional dependencies and linguistic interpretations employed in knowledge discovery tasks from relational databases

  • Original language description

    Knowledge discovery from databases copes with several problems including the heterogeneity of data and interpreting the solution in an understandable and convenient form for domain experts. Fuzzy logic approaches based on the computing with words paradigm are very appealing since they offer the possibility to express useful knowledge from a large volume of data by linguistic terms, which are easily understandable for diverse users. In this paper, the novel descriptive data mining algorithm based on fuzzy functional dependencies has been proposed. In the first step, data are fuzzified, which ensures the same manipulation of crisp and fuzzy data. The data mining step is based on revealing fuzzy functional dependencies among considered attributes. In the final step, the mined knowledge is interpreted linguistically by the fuzzy modifiers and quantifiers. The proposed algorithm has been explained on illustrative data and tested on real-world dataset. Finally, its benefits, weak points and possible future research topics are discussed.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

  • ISSN

    0952-1976

  • e-ISSN

  • Volume of the periodical

    88

  • Issue of the periodical within the volume

    February 2020

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    15

  • Pages from-to

    103395

  • UT code for WoS article

    000510523600019

  • EID of the result in the Scopus database

    2-s2.0-85075551235