基于不确定数据的可能频繁闭序列模式挖掘  

Mining probabilistically frequent closed sequential patterns in uncertain databases

作　　者：李立波[1,2] 白树仁[1,2] 陈磊[1,2] 张威[1,2] 

机构地区：[1]湖南大学信息科学与工程学院,长沙410082 [2]国家超级计算长沙中心,长沙410082

出　　处：《计算机应用研究》2016年第4期983-988,共6页Application Research of Computers

基　　金：国家科技支撑计划资助项目(2012BAH09B02);长沙市重点科技计划资助项目(K1204006-11-1)

摘　　要：对于不确定数据的频繁序列模式挖掘,会导致可能频繁模式数量的指数级出现,其中有些无用的挖掘结果会引起频繁序列的冗余。针对上述不足,提出了可能频繁闭序列模式(p-FCSPs)的定义,以及一种基于不确定数据的可能频繁闭序列挖掘算法U-FCSM。此算法中,基于一种元组不确定数据模型计算序列的可能频繁性,应用BIDE算法的闭序列思想判断可能频繁序列是否是可能频繁闭序列模式;为了减少搜索空间与避免冗余的计算,应用了几个剪枝与边界技术。U-FCSM算法的有效性与效率通过大量的实验得以表明。Frequent sequential patterns mining in uncertain data can lead an exponential number in probabilistically frequent sequence patterns,which contains some useless mining results and causes redunancy of frequent sequence. Regarding to the above disadvantages,this paper put forword a definition of probabilistically frequent closed sequential patterns（ p-FCSPs）,and proposed a mining algorithm of p-FCSPs based on uncertain data,called U-FCSM. Based on a tuple uncertain data model,this algorithm calculated the possibility of frequent sequences,and then judged whether probabilistically frequent sequences was pFCSPs according to the idea of closed sequence of BIDE algorithm principle. In order to reduce the search space and avoid redundant computation,it applied several pruning and boundary techniques. Finally,extensive experiments show that the effectiveness and efficiency of U-FCSM.

关 键 词：不确定数据 可能频繁闭序列模式 概率频繁 不确定数据挖掘 闭序列模式 序列模式增长 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]