Incremental Frequent Itemsets Mining With FCFP Tree
Incremental Frequent Itemsets Mining With FCFP Tree
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DOI：
10.1109/access.2019.2943015
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发表时间：
2019-09
期刊：
影响因子：
3.9
通讯作者：
Jiaojiao Sun;Yaling Xun;Jifu Zhang;Junli Li
中科院分区：
文献类型：
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作者：
Jiaojiao Sun;Yaling Xun;Jifu Zhang;Junli Li
频繁项集挖掘（FIM）和其他挖掘技术已经受到大规模和快速扩展数据集的挑战。为了解决这个问题，我们提出了一个使用全压缩频繁模式树（FCFP-Tree）和相关算法FCFPIM的增量频繁项集挖掘解决方案。与FP-tree不同，FCFP-Tree保持原始数据集中所有频繁和不频繁项目的完整信息。这允许FCFPIM算法在添加新数据集和支持更改时不会浪费先前处理的原始数据集的任何扫描和计算开销。因此，节省了大量的处理时间。重要的是，FCFPIM采用了一种有效的树结构调整策略，当一些项目的支持由于新数据的到来而发生变化时。FCFPIM有利于加快增量FIM的性能。虽然包含无损项信息的树结构占用空间，但采用压缩策略来节省空间。实验结果表明，在支持阈值较低的情况下，为了获得执行效率的提高，占用空间是值得的。
Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression Frequent Pattern Tree (FCFP-Tree) and related algorithms called FCFPIM. Unlike FP-tree, the FCFP-Tree maintains complete information of all the frequent and infrequent items in the original dataset. This allows the FCFPIM algorithm not to waste any scan and computational overhead for the previously processed original dataset when new dataset are added and support changes. Therefore, much processing time is saved. Importantly, FCFPIM adopts an effective tree structure adjustment strategy when the support of some items changes due to the arrival of new data. FCFPIM is conducive to speeding up the performance of incremental FIM. Although the tree structure containing the lossless items information is space-consuming, a compression strategy is used to save space. We conducted experiments to evaluate our solution, and the experimental results show the space-consuming is worthwhile to win the gain of execution efficiency, especially when the support threshold is low.