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http://dx.doi.org/10.3745/KIPSTD.2005.12D.6.817

Efficient Mining of Frequent Itemsets in a Sparse Data Set  

Park In-Chang (삼성전자)
Chang Joong-Hyuk (연세대학교 소프트웨어응용연구소)
Lee Won-Suk (연세대학교 컴퓨터과학과)
Publication Information
The KIPS Transactions:PartD / v.12D, no.6, 2005 , pp. 817-828 More about this Journal
Abstract
The main research problems in a mining frequent itemsets are reducing memory usage and processing time of the mining process, and most of the previous algorithms for finding frequent itemsets are based on an Apriori-property, and they are multi-scan algorithms. Moreover, their processing time are greatly increased as the length of a maximal frequent itemset. To overcome this drawback, another approaches had been actively proposed in previous researches to reduce the processing time. However, they are not efficient on a sparse .data set This paper proposed an efficient mining algorithm for finding frequent itemsets. A novel tree structure, called an $L_2$-tree, was proposed int, and an efficient mining algorithm of frequent itemsets using $L_2$-tree, called an $L_2$-traverse algorithm was also proposed. An $L_2$-tree is constructed from $L_2$, i.e., a set of frequent itemsets of size 2, and an $L_2$-traverse algorithm can find its mining result in a short time by traversing the $L_2$-tree once. To reduce the processing more, this paper also proposed an optimized algorithm $C_3$-traverse, which removes previously an itemset in $L_2$ not to be a frequent itemsets of size 3. Through various experiments, it was verified that the proposed algorithms were efficient in a sparse data set.
Keywords
Frequent Itemse; Sparse Data Set; null; null; null;
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