计算机科学与探索Issue(2)：172-181,10.DOI:10.3778/j.issn.1673-9418.1405050
不确定数据流上Top-k异常点查询算法
Top-k Outlier Detection Algorithm on Uncertain Data Stream
摘要
Abstract
In recent years, along with the appearance of uncertain data, outlier detection on uncertain data stream becomes a new hotspot. However, three parameters are contained in the existing definition of outlier on uncertain data, it is very difficult for users to set these parameters, the user cannot get the suitable outlier. Most of the time, the users would like to get the objects which are most likely to be outliers. This paper proposes the top-k outlier detection on uncertain data stream. The proposed method prunes objects based on the estimation of the range of probabilities being outlier and reduces some unnecessary computation. Meanwhile, this paper proposes the incremental method for computing the range of probabilities to improve efficiency. Finally, the performance of the proposed method is veri-fied through a number of simulation experiments on real and synthetic datasets.关键词
不确定数据/数据挖掘/异常点/top-kKey words
uncertain data/data mining/outlier/top-k分类
信息技术与安全科学引用本文复制引用
曹科研,王国仁,韩东红,李硕儒..不确定数据流上Top-k异常点查询算法[J].计算机科学与探索,2015,(2):172-181,10.基金项目
The National Natural Science Foundation of China under Grant Nos.61025007,61328202,61173029(国家自然科学基金) （国家自然科学基金）
