Apriori算法改进及在超市数据挖掘中应用

Improved of Apriori algorithm and Appl ication of Data Mining in Supermarket

在超市购物数据处理时,常用Apriori算法对顾客"购物篮"进行分析;由于计算量大,Apriori算法通过使用最小支持度阈值和剪枝技术,控制了频繁集项数,但仍然有可能出现意义不大的频繁项集。将商品分成几个大类,设置大类的加权值;然后,计算加权支持度和置信度,根据最小加权支持度和置信度阈值进行剪枝和筛选,获得新的频繁项集和关联规则,提高算法的效率。

On the time of shopping data processing in super market, Apriori algorithm husually is used in the analysis of shopping basket; because of the cost of computing, Apriori uses the minimum support value and prune method to control the quantity of frequent items. However, there are still some frequent items of low significance. This paper divided the commodities to some big classes with weighting, then computed the weighting support value and confidence value which are used to prune and filter for new frequent items and associate rules. The efficiency of algorithm is improved.