Algorithm for defuzzification of multi-valued taxonomic attributes in similarity-based fuzzy relational databases
نویسندگان
چکیده
In this work we investigate our potential ability to discover knowledge from multi-valued attributes (often referred in literature on fuzzy databases as fuzzy collections [1-3]), that have been utilized in fuzzy relational database models [4-7] as a convenient way to represent uncertainty about the data registered in the data tables. We present here implementation details and extended tests of a heuristic algorithm, which we used in the past [8-11] to interpret non-atomic values stored in a fuzzy relational database. In our evaluation we consider different data imprecision levels, as well as diverse shapes of similarity hierarchies.
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