In Chang Park , Joong Hyuk Chang , Won Suk Lee

The KIPS Transactions:PartD, Vol. 12, No. 6, pp. 817-828, Dec. 2005
10.3745/KIPSTD.2005.12.6.817,   PDF Download:

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 L2-tree, was proposed first, and an efficient mining algorithm of frequent itemsets using L2-tree, called an L2-traverse algorithm, was also proposed. An L2-tree is constructed from L2, i.e., a set of frequent itemsets of size 2, and an L2-traverse algorithm can find its mining result in a short time by traversing the L2-tree once. To reduce the processing more, this paper also proposed an optimized algorithm C3-traverse, which removes previously an itemset in L2 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.

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