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A high coherent utility fuzzy itemsets mining algorithm

机译:高相干效用模糊项集挖掘算法

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摘要

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, it will be considered as a High Utility Fuzzy Itemset (HUFI). Finally, contingency tables are calculated and used for checking those HUFI satisfy specific four criteria or not. If yes, it is a High Coherent Utility Fuzzy Itemsets (HCUFI). Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.
机译:本文提出了一种从命题逻辑性质的定量交易中挖掘高连贯效用模糊项集(HCUFI)的算法。它首先将定量交易转换为模糊集。然后,根据给定的外部效用表计算每个模糊项集的效用。如果该值大于或等于最小使用率,则将其视为高使用率模糊项集(HUFI)。最后,计算列联表,并将其用于检查那些HUFI是否满足特定的四个标准。如果是,则为高相干效用模糊项集(HCUFI)。还对foodmart数据集进行了实验,以证明该算法的有效性。

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