Fuzzy functional dependencies and linguistic interpretations employed in knowledge discovery tasks from relational databases
| Autor: | Miljan Vucetic, Boško Božilović, Miroslav Hudec |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Descriptive statistics Fuzzy functional dependency Computer science Relational database 02 engineering and technology Fuzzy logic Linguistics Domain (software engineering) 020901 industrial engineering & automation Knowledge extraction Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering |
| Zdroj: | Engineering Applications of Artificial Intelligence. 88:103395 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2019.103395 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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