IMR OpenIR
Continuous Outlier Monitoring on Uncertain Data Streams
Alternative TitleContinuous Outlier Monitoring on Uncertain Data Streams
Cao KeYan1; Wang GuoRen1; Han DongHong1; Ding GuoHui2; Wang AiXia1; Shi LingXu3
2014
Source PublicationJOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN1000-9000
Volume29Issue:3Pages:436-448
AbstractOutlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection (CUOD), which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach Probability Pruning for Continuous Uncertain Outlier Detection (PCUOD) to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.
Other AbstractOutlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection (CUOD), which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach - Probability Pruning for Continuous Uncertain Outlier Detection (PCUOD) to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.
KeywordPROBABILISTIC DATA outlier detection uncertain data stream data mining parameter variable query
Indexed ByCSCD
Language英语
Funding Project[National Natural Science Foundation of China] ; [National High Technology Research and Development 863 Program of China] ; [National Basic Research 973 Program of China]
CSCD IDCSCD:5130627
Citation statistics
Cited Times:4[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/155274
Collection中国科学院金属研究所
Affiliation1.东北大学
2.中国科学院金属研究所
3.Logist Engn University Peoples Liberat Army, Dept Command Informat Syst Engn, Chongqing 400311, Peoples R China
Recommended Citation
GB/T 7714
Cao KeYan,Wang GuoRen,Han DongHong,et al. Continuous Outlier Monitoring on Uncertain Data Streams[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2014,29(3):436-448.
APA Cao KeYan,Wang GuoRen,Han DongHong,Ding GuoHui,Wang AiXia,&Shi LingXu.(2014).Continuous Outlier Monitoring on Uncertain Data Streams.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,29(3),436-448.
MLA Cao KeYan,et al."Continuous Outlier Monitoring on Uncertain Data Streams".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 29.3(2014):436-448.
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