计算机工程与应用2019，Vol.55Issue(19)：105-114,140,11.DOI:10.3778/j.issn.1002-8331.1807-0150
FODU:不确定数据集中快速离群点检测方法
FODU:Fast Outlier Detection Approach on Uncertain Data Sets
摘要
Abstract
Outlier detection is a hot topic in the field of data management, which has been widely applied to many fields such as medical diagnosis, financial fraud, environment monitoring and many others. At present, along with the application of sensors in data acquisition, people have realized the universality of uncertain data in many fields. Compared with certain data, it is much more difficult to detect outliers on uncertain data sets. To solve the problems, a Fast Outlier Detection approach on Uncertain data sets(FODU)is proposed. Firstly, an index construction strategy inspired by hierarchical ideas is given, which not only overcomes the limitation of the traditional index structure on multi-dimensional data management, but also can prune the searching space quickly. Furthermore, to detect uncertain outliers efficiently, a new filtering algorithm is proposed. Utilizing batch filtering and single point filtering, this approach can reduce redundant calculations and improve inspection efficiency. Then, to avoid the expansion of the possible world, an approach to compute the abnormal probability of data objects is given. At last, the efficiency and effectiveness of the proposed approaches are verified through a series of simulation experiments. The experimental results show that compared with the previous approaches, the proposed algorithm can significantly improve the computation efficiency of outlier detection on uncertain data.关键词
离群点检测/不确定性数据/分层次划分/批量过滤Key words
outlier detection/uncertain data/hierarchical partitioning/batch filtering分类
信息技术与安全科学引用本文复制引用
钟毓灵,王习特,白梅,朱斌,李冠宇..FODU:不确定数据集中快速离群点检测方法[J].计算机工程与应用,2019,55(19):105-114,140,11.基金项目
国家自然科学基金青年基金(No.61602076,No.61702072) （No.61602076,No.61702072）
中国博士后科学基金面上项目(No.2017M611211,No.2017M621122) （No.2017M611211,No.2017M621122）
中央高校基本科研业务费专项资金(No.3132018191) （No.3132018191）
国家重点研发计划项目(No.2017YFC1404606). （No.2017YFC1404606）
