A high coherent utility fuzzy itemsets mining algorithm

Chun-Hao Chen, Ai-Fang Li, Yeong-Chyi Lee
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Abstract

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.
一种高相干实用模糊项集挖掘算法
提出了一种从具有命题逻辑性质的定量交易中挖掘高相干效用模糊项集(HCUFI)的算法。它首先将定量交易转化为模糊集。然后,根据给定的外部效用表,计算每个模糊项目集的效用。如果该值大于或等于最小效用比,则将其视为高效用模糊项集(High utility Fuzzy Itemset, HUFI)。最后,计算列联表,用于检验HUFI是否满足特定的四个标准。如果是,它是一个高相干效用模糊项集(HCUFI)。在foodmart数据集上进行了实验,验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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