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In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose common-sense...
In this paper, we propose an algorithm for mining high coherent utility fuzzy itemsets (HCUFI) from quantitative transactions with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, utility of each fuzzy itemsets is then calculated according to the given external utility table. If the value is large than or equals to the minimum utility ratio,...
Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested...
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