Vertical Mining Probabilistic Frequent Itemsets From Uncertain Database
With the emergence of new applications,the traditional methods of mining frequent itemsets are confronted with enormous challenges in uncertain environment.As one of the most classic algorithms,Eclat algorithm is regarded as a promising approach whose effciency is experimentally proved in mining expected support-based frequent itemsets,while it has not received much attention in the area of mining probabilistic frequent itemsets.In this paper,we review the previous effcient algorithms in the research of frequent itemsets mining over uncertain database,and then we propose a vertical mining algorithm with Eclat framework,which can mine all probabilistic frequent itemsets exactly and effciently.In order to study its performance,we design difierent experiments to evaluate its performance on both synthetic and real data sets,which shows that the vertical mining algorithm is simple to implement and perform well in practice.
frequent itemsets mining uncertain database algorithm data mining
Xiao-mei YU Hong Wang Xiangwei Zheng
School of Information Science and Engineering,Shandong Normal University,Jinan 250014,P.R.China Shandong Provincial Key Laboratory for Distributed Computer software Novel Technology
国内会议
济南
英文
5-12
2014-10-16（万方平台首次上网日期，不代表论文的发表时间）