Mining Quantitative Association Rules with Multiple Minimum Supports

• Main Authors: Chia-hui Zhuang, 莊嘉慧
• Other Authors: Yen-ju Yang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/95657691206033209854

碩士 === 大同大學 === 資訊經營學系(所) === 93 === The past research of mining quantitative association rules is aim to use fuzzy value like large quantity, small quantity, etc. to express quantitative attribute. It is difficult to design a bundle of items for sales promotion. In Chen’s paper [3], an Apriori-based algorithm, named MQA-1, is developed to mine association rules in bag database. However, using only one minimum support can’t reflect the nature of items. In this paper, we propose a FP-tree-like structure to store all information about itembag and an efficient algorithm to mine quantitative association rules with multiple minimum supports. It form looks like “milk = 2 �_ bread = 3”, then we can combine three units of milk with two units of bread to form bundling. For decision makers, it is easy and precise to make decisions with quantity information.