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首页> 外文期刊>Journal of Computers >An Efficient Mining Algorithm by Bit Vector Table for Frequent Closed Itemsets
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An Efficient Mining Algorithm by Bit Vector Table for Frequent Closed Itemsets

机译：一种基于位向量表的频繁封闭项目集高效挖掘算法。

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摘要

Mining frequent closed itemsets in data streams is an important task in stream data mining. In this paper, an efficient mining algorithm (denoted as EMAFCI) for frequent closed itemsets in data stream is proposed. The algorithm is based on the sliding window model, and uses a Bit Vector Table (denoted as BVTable) where the transactions and itemsets are recorded by the column and row vectors respectively. The algorithm first builds the BVTable for the first sliding window. Frequent closed itemsets can be detected by pair-test operations on the binary numbers in the table. After building the first BVTable, the algorithm updates the BVTable for each sliding window. The frequent closed itemsets in the sliding window can be identified from the BVTable. Algorithms are also proposed to modify BVTable when adding and deleting a transaction. The experimental results on synthetic and real data sets indicate that the proposed algorithm needs less CPU time and memory than other similar methods.
机译：在数据流中挖掘频繁关闭的项目集是流数据挖掘中的重要任务。本文提出了一种有效的挖掘算法（称为EMAFCI），用于数据流中频繁关闭的项目集。该算法基于滑动窗口模型，并使用位向量表（表示为BVTable），其中事务和项集分别由列和行向量记录。该算法首先为第一个滑动窗口构建BVTable。可以通过对表中的二进制数进行配对测试操作来检测频繁关闭的项目集。建立第一个BVTable之后，算法会为每个滑动窗口更新BVTable。可以从BVTable中识别出滑动窗口中频繁关闭的项目集。还提出了在添加和删除事务时修改BVTable的算法。综合和真实数据集的实验结果表明，与其他类似方法相比，该算法所需的CPU时间和内存更少。

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