会议论文

An Efficient Frequent Closed Itemsets Mining Algorithm Over Data Streams

中文

2017

197-201

2011 Second International Conference on Information,Communication and Education Application(ICEA2011)(2011年第二届信息、通信与教育应用国际学术研讨会)

2011 Second International Conference on Information,Communication and Education Application(ICEA2011)(2011年第二届信息、通信与教育应用国际学术研讨会)论文集

20111014

上海

美国信息工程研究会

本校为第一机构

挖掘频繁闭集是数据流上知识发现的最重要的问题之一.本文首先运用概念格表示闭集,然后提出一种新颖的基于滑动窗口的动态更新频繁闭集的算法,有效缩小了频繁闭集的搜索空间.在仿真数据集上的实验表明该算法在执行时间上比Moment算法具有更优的性能.

　　Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams.In this paper, we proposed a novel sliding window based algorithm.The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction.Experiments results on synthetic datasets show that our prop...