Algorithm for Improvement of the Performance of Apriori Algorithm

摘要：

Apriori Algorithm is one of the classical algorithms for finding association rules, and is widely used in various applications such as Market basket analysis, Fraud detection, Early warning of equipment failure etc. Apriori algorithm has two limitations; number of times database scanned is too large and number of candidate itemsets generated is large. To reduce these two limitations of Apriori algorithm a method is purposed in this paper. The proposed method is based on the property if a set is frequent then all its subsets must also be frequent In this we directly generate the frequent item subsets from frequent itemsets and then apply dataset reduction method to find the support for each frequent itemsets. It is found that this proposed algorithm greatly improves the performance; it reduces the number of times database is scanned and the number of candidate itemsets generated.

关键词： Apriori algorithm Association rule Frequent itemsets

作者: Munish Saini Mrs.Geeta Sikka

作者单位: Department of Computer Science and Technology National Institute of Technology Jalandhar, India

会议类型: 国际会议

会议名称: 2011 International Conference on Database and Data Mining(ICDDM 2011)(2011年数据库和数据挖掘国际会议)

会议地点: 三亚

会议语种:英文

页码: 153-156

在线出版日期: 2011-03-25（万方平台首次上网日期，不代表论文的发表时间）