An efficient association rules mining algorithm and its application in mining weighted quantitative association rules with multiple support threshold

• Main Authors: Show-Ju Chen, 陳秀如
• Other Authors: Jen-peng Huang
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/94659598291095856971

碩士 === 南台科技大學 === 資訊管理系 === 92 === The generation of frequent itemsets is an essential and time-consuming step in mining association rules. Most of the studies adopt the Apriori-based approach, which has great effort in generating candidate itemsets and needs multiple database accesses. Recent studies indicate like FP-Tree approach has been utilized to avoid the generation of candidate itemsets and scan transaction database only twice, but they work with more complicated data structure. Therefore, algorithms for efficient mining of frequent patterns are in urgent demand. This thesis aims to improve both time and space efficiency in mining frequent itemsets. We propose a novel QSD(Quick Simple Decomposition) algorithm using simple decompose principle which derived from minimal heap tree, we can discover the frequent itemsets quickly under once database scan. Meanwhile, QSD algorithm is not necessary to rescan database and reconstruct data structure when database is updated or minimum support is varied. Moreover, we apply the features of the QSD algorithm to explore profit weight-based quantitative association rules with multiple support threshold in accordance with item’s characteristics. The derived rule can solve the problem that itemset with high profit but few trading times was difficult to find out. Comprehensive experiments have been conducted to assess the performance of the proposed algorithm. Experimental results show that the QSD algorithm outperform previously ones, like ICI, FP-Tree algorithms etc.