【Key words】 Data stream; Outlier detection; Heterogeneous attributes; Closed frequent itemsets; Sliding window
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closed constrained frequent itemsets 闭约束频繁项集
global frequent closed itemsets 全局频繁闭项目集
minimum frequent closed itemsets 最小频繁闭项目集
maximal frequent closed itemsets 最大频繁闭项目集
minimum strong frequent closed itemsets 最小强频繁闭项目集
So mining closed frequent itemsets and their lattice algorithms is one of the important research fields of association rules mining.
因此用挖掘频繁闭项集及其格结构算法来快速高效的产生关联规则是一个重要的研究方向。
参考来源 - 挖掘频繁闭项集并构建其格的快速算法研究Experimental results show that it can quickly and efficiently mine the 3D data set of the closed frequent itemsets.
实验结果表明,与Data-Peeler算法相比,该算法可以更快速有效地挖掘出三维数据集中的闭频繁项集。
参考来源 - 三维数据集中基于位运算的挖掘算法·2,447,543篇论文数据，部分数据来源于NoteExpress
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