A Novel Method of Mining Frequent Item Sets

摘要：

The aim of mining association rules is to discover the association relationship among the item sets from mass data. In some practical applications, its role is mainly to assist decisionmaker. The paper proposes a novel association rule algorithm of mining frequent item sets, which introduces a new data structure and adopts compressed storage tree to improve the run performance of this algorithm. At last, the experiment indicates that the algorithm proposed in this paper has much more advantages in load balance and run time compared with most existing algorithms.

关键词： Data Mining Association Rules Frequent Item Sets

作者: Dong Liyan Liu Zhaojun Shi Mo Yan Pengfei Tian Zhuo Li Zhen

作者单位: College of Computer Science and Technology,Jilin University,Changchun,130012,China Changchun Taxation College,Changchun,130117,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）