Mining Quantitative Association Rules with Density Constraint

• Main Author: 郭瑞男
• Other Authors: 柯佳伶
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/13322880493417591245

碩士 === 國立臺灣師範大學 === 資訊教育研究所 === 91 === A new approach, called PQAR (Partition-based Quantitative Association Rules mining) algorithm, is proposed in this thesis for mining quantitative association rules. This approach finds out all the frequent interval itemsets that satisfy the minimum relative density requirement based on space partitioning method, and the quantitative association rules are produced from these interval itemsets. When mining frequent interval itemsets, PQAR algorithm considers not only the minimum support as the filtering condition, but also the minimum relative density to prevent finding the intervals in which data distribution is sparse. In addition, based on space partitioning method to find out the largest intervals that meet the threshold requirements, the number of qualified intervals is reduced such that the resulting rules are significant and concise. Furthermore, because the number of times to scan database is reduced possibly in PQAR algorithm, the mining time is shorten considerably than the previous approaches. The experimental results show that, when testing data sets with various supports and relative densities setting, PQAR algorithm obtains results with high accuracy and recall in most cases. Moreover, under the same accuracy condition, PQAR algorithm takes much less time than QAR algorithm.