一种带记忆性的零售商品关联度分析方法  

An approach for retail goods association rules analysis with memory property

作　　者：杨丰梅[1] 李梦[1] 田歆[2] 李健[3] 丁玉章[2,4] 

机构地区：[1]北京化工大学理学院,北京100029 [2]中国科学院虚拟经济与数据科学研究中心,北京100190 [3]北京化工大学经济管理学院,北京100029 [4]上海海鼎信息工程股份有限公司,上海201112

出　　处：《系统工程理论与实践》2014年第11期2872-2880,共9页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(71202114;71171011;71390330);中央高校基础科研业务费专项基金(zz1133)

摘　　要：基于关联规则的购物篮分析对实体或在线零售企业改善品类管理、优化门店布局陈列、实现关联促销与推荐等起着重要作用.本文针对企业需求,应用邻接矩阵和截矩阵技术,提出了挖掘商品关联性的AC算法和进一步求频繁k-项集的M-AC算法,证明了这类方法运算简单,应用成本低,具有记忆性质,当数据更新时,无须重新运算.通过在零售企业的成功应用,论证了这种商品关联度分析方法的有效性.The market basket analysis based on association rules is a powerful tool for physical and online retailers to stimulate consumption and increase profits via improving strategies on category management, store layout, associated promotions and recommendations. According to this demand, we propose AC algorism to get association rules and M-AC algorism which is used to obtain frequent k-itemset based on adjacency matrix and cut matrix technology. This method possesses such merits as simple and efficient operation, low cost, and memory property （meaning that there is no need to operate again when data updates）. The effectiveness of this new method is proved by its successful application in a retail chains.

关 键 词：购物篮分析 关联规则 频繁项目集 商业智能 邻接矩阵 

分 类 号：O22[理学—运筹学与控制论]