正负关联规则挖掘算法在电子商务网站中的应用

摘要 为了增加电子商务网站的销售额，分析了商品推荐在电子商务系统中的作用，研究了数据挖掘中的正负关联规则挖掘技术的特点，提出了一种新的正负关联规则挖掘算法。新算法通过使用旋转数据库和位运算减少了扫描原始数据库的次数，加快了挖掘速度。另外，新算法在挖掘正规则的同时，利用负规则裁减掉无效规则，得到的结果更符合用户的购物行为。通过测试得出新算法比传统的Apriori算法执行时间更短，商品推荐更精确。

Application of Positive and Negative Association Rules in E-Commerce

Abstract] In order to increase sales of e-commerce site, we analyzed the role of product recommendation in e-commerce system, studied the characteristics of association rules in data mining, and proposed a new algorithm for mining positive and negative association rules. The new algorithm reduces the number of scanning the original database through using rotary database and bit operations, and accelerates the speed of data mining. In addition, when the new algorithm is mining positive association rules, it cuts out invalid rules using negative rules, and this makes the result more in line with the user's shopping behavior. Obtained by testing, the execution time of the new algorithm is shorter than the traditional Apriori algorithm, and product recommendation is more accurate.