知识与财富的链接
不确定数据普遍存在于如移动计算、RFID技术和传感器网络等大量应用之中.由于不确定数据的离群点检测算法可以提高服务质量,提出一种基于密度的不确定数据离群检测算法RLOF.该算法引入一种R2-tree结构,有效降低了计算局部离群因子时的时间复杂度,同时降低了不确定数据集中的数据更新成本以及海量数据维护成本.理论分析和实验结果充分证明了该算法是有效可行的.
Uncertain data generally exist in a large number of applications,such as mobile computing,sensor networks and RFID technology.Outliers detection algorithm can improve the quality of these services.An uncertain data outlier detection algorithm based on density RLOF was proposed.This algorithm introduces a R2-tree structure,which effectively reduces the time complexity when calculating local outlier factor.It also reduces the cost of data updating in the uncertain data set and the maintenance cost of a massive data.The theoretical analysis and experimental results fully prove that the algorithm is effective and feasible.
