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Web usage mining with evolutionary extraction of temporal fuzzy association rules

机译：Web使用挖掘与时间模糊关联规则的进化提取

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In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The CA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algo rithms, because it discovers extra rules.
机译：在Web使用情况挖掘中，具有时间属性的模糊关联规则可以提供有关何时发生关联的有用知识。但是，传统的时间模糊关联规则挖掘算法存在问题。一些规则发生在模糊集边界的交集处，该交集的支持较少（隶属度较低），因此这些规则会丢失。描述了一种基于遗传算法（GA）的解决方案，该解决方案使用2元组语言表示形式的灵活特性来发现在模糊集边界的交集处发生的规则。基于CA的方法通过包含图形表示和改进的适应度功能而得到了增强。将基于GA的方法与传统方法在现实世界Web日志数据上的比较发现，规则被传统方法所丢失。推荐使用基于GA的方法来补充现有算法，因为它会发现其他规则。

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