Fuzzy utility mining with upper-bound measure

• We introduce a new fuzzy utility function to evaluate the fuzzy utilities of itemsets.
• The fuzzy minimum operator is applied to the proposed fuzzy utility function.
• An effective model is designed to keep downward-closure property.
• A two-phase fuzzy utility mining algorithm, namely TPFU, is proposed.
• The experiments in synthetic and real dataset prove the practicality of TPFU.

Fuzzy utility mining has been an emerging research issue because of its simplicity and comprehensibility. Different from traditional fuzzy data mining, fuzzy utility mining considers not only quantities of items in transactions but also their profits for deriving high fuzzy utility itemsets. In this paper, we introduce a new fuzzy utility measure with the fuzzy minimum operator to evaluate the fuzzy utilities of itemsets. Besides, an effective fuzzy utility upper-bound model based on the proposed measure is designed to provide the downward-closure property in fuzzy sets, thus reducing the search space of finding high fuzzy utility itemsets. A two-phase fuzzy utility mining algorithm, named TPFU, is also proposed and described for solving the problem of fuzzy utility mining. At last, the experimental results on both synthetic and real datasets show that the proposed algorithm has good performance.

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