BitApriori: An Apriori-Based Frequent Itemsets Mining Using Bit Streams

Thi Thanh Nhan Le, Thi Thanh Thuy Nguyen, Tae-Choong Chung
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引用次数: 9

Abstract

Generating, pruning and counting itemset candidates are important steps in Apriori frequent itemset mining. Unfortunately, their computation time are too expensive. In this paper, we propose a new method using Bit Stream to improve their speed. At the begining, the 1-itemsets are found out and sorted according to the decline of count. By that way, a map of all attributes would be created. After that, each attribute will be presented by 1 bit. At last, the generating and pruning itemset candidates are processed by LOGIC operations which are not cost much of computation time. For experiments we compare our method with some Apriori-based state of the arts.
BitApriori:基于apriori的使用比特流的频繁项集挖掘
候选项集的生成、修剪和计数是Apriori频繁项集挖掘的重要步骤。不幸的是,它们的计算时间太昂贵。在本文中,我们提出了一种利用比特流来提高其速度的新方法。首先,根据计数的递减找出1项集并进行排序。通过这种方式,将创建所有属性的映射。之后,每个属性将以1位表示。最后,候选项集的生成和剪枝由逻辑运算处理,节省了大量的计算时间。对于实验,我们将我们的方法与一些基于先验的技术进行比较。
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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