Vertical Mining Probabilistic Frequent Itemsets From Uncertain Database

• 作 者: Xiao-meiYU;HongWang;XiangweiZheng;
• 机 构: School of Information Science and Engineering,Shandong Normal University,Jinan 250014,P.R.China Shan
• 出 处: 2014全国理论计算机科学学术年会
• 发表日期: 2014年期

　　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.