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Fuzzy utility mining with upper-bound measure
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DOI：10.1016/j.asoc.2015.01.055.png)
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摘要
En 中文
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. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Data mining
Fuzzy data mining
Fuzzy utility mining
High fuzzy utility itemset
Upper bound
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6.6
论文数:
1.4W
被引数:
4.8W
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