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Frequent Itemset Mining in Vertical Layout with E-ACO Algorithm: In Super Market

机译:用E-ACO算法常用术语在垂直布局中挖掘:超级市场

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摘要

Association Rule Mining is a well-liked in data mining. Mostly Apriori algorithm is used for market basket analysis but this Apriori algorithm have few limitations like scan database again and again for finding frequent itemset, more access time.etc. This limitation is reduced by using vertical datasets in Eclat algorithm with ACO Technique is called E-ACO algorithm. We propose in this paper, memory and time saving inspiration is discussed. Our detecting the method prove that techniques shows good performance of memory and running time optimization the number of rules generated.
机译:协会规则挖掘是一种充足的数据挖掘。大多数APRiori算法用于市场篮子分析,但此APRIORI算法再次允许扫描数据库的限制很少,再次用于查找频繁的项目集,更多Access Time.Etc。通过在ECLAT算法中使用具有ACO技术的垂直数据集来减少这种限制称为E-ACO算法。我们提出了本文,讨论了记忆和时间的灵感。我们检测方法证明了该技术显示了内存和运行时间优化的良好性能,所产生的规则数量。

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