Abstract
AbstractMany databases in real life involve items with their quantities. This kind of databases can be modeled using the theory of bags or by fuzzy bags if we deal with imprecise properties of objects. We present a general framework for extracting useful knowledge from fuzzy bags or more generally from RL-bags, a new type of bag which extends the one of fuzzy bag and preserves the usual crisp properties overall when using the negation. The main contribution is how to deal with the information provided with the RL-bags for then mining useful and interesting association rules, as the RL-bags involve uncertainty over the quantities associated to the objects.KeywordsbagsRL-bagsRL-setsRL-representationsfuzzy rules
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