不确定数据流上的离群点检测处理  

Outlier Detection on Uncertain Data Streams

作　　者：朱斌[1] 钟毓灵 王习特 白梅 ZHU Bin;ZHONG Yuling;WANG Xite;BAI Mei(College of Information Science and Technology,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连海事大学信息科学技术学院,辽宁大连116026

出　　处：《湖南大学学报（自然科学版）》2020年第2期134-140,共7页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(61602076,61702072,61976032);中国博士后科学基金资助项目(2017M611211,2017M621122);辽宁省自然科学基金资助项目(20180540003);赛尔网络下一代互联网技术创新项目(NGII20190902);国家重点研发计划项目(2017YFC1404606);中央高校基本科研业务费专项基金资助项目(3132019202)。

摘　　要：提出了一种快速不确定数据流上的离群点检测算法.采用分层次划分思想给出了适用于流式数据的索引构建方法,并为索引结构中的叶子结点增加了部分存储信息,使得在数据更新时新流入的数据点可以利用中间结果信息直接完成批量过滤,降低计算成本.通过分析离群概率值求解的递推规律,给出了一种全新的离群概率值求解方案,该方案可以最大可能地避免全近邻集合的迭代计算,减少了大量的非离群点计算代价,从而加快处理速度.实验结果表明,快速不确定数据流上的离群点检测算法能够有效地提高检测效率.This paper proposed a Fast Outlier Detection algorithm Over Uncertain Data Streams(FOD_OUDS).Firstly,an index inspired by hierarchical ideas was designed to manage uncertain data stream,and some storage information was added for the leaf nodes in the index structure,so that the newly inflowed data points can directly perform batch filtering by using the intermediate result information when the data is updated,thereby achieving the purpose of reducing the calculation cost.Secondly,by analyzing the recursive rules of outlier probability values in calculation,a novel outlier probability value solution scheme was presented,which can avoid as much as possible the calculating cost of nearest neighbor set,reduce the processing cost of a large number of inliers,thus speeding up processing.At last,a large amount of experiments show that the FOD_OUDS algorithm can effectively improve the detection efficiency.

关 键 词：离群点 不确定数据流 滑动窗口 过滤策略 分层次划分 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]