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

Abstract

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.