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Continuous Outlier Monitoring on Uncertain Data Streams

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文摘 Outlier 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.
来源 Journal of Computer Science and Technology ,2014,29(3):436-448 【核心库】
DOI 10.1007/s11390-014-1441-x
关键词 outlier detection ; uncertain data stream ; data mining ; parameter variable query
地址

1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819  

2. College of Information Science and Engineering, Northeastern University, Key Laboratory of Medical Image Computing, Shenyang, 110819  

3. College of Computer, Shenyang Aerospace University, Shenyang, 110819  

4. Department of Command Information System Engineering, Logistic Engineering University of People's Liberation Army, Chongqing, 400311

语种 英文
文献类型 研究性论文
ISSN 1000-9000
学科 自动化技术、计算机技术
基金 国家自然科学基金 ;  国家863计划 ;  国家973计划
文献收藏号 CSCD:5130627

参考文献 共 26 共2页

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引证文献 5 篇

1 Han Donghong Classifying Uncertain and Evolving Data Streams with Distributed Extreme Learning Machine Journal of Computer Science and Technology,2015,30(4):874-887
CSCD被引 1 次

2 Wang Xite An Efficient Algorithm for Distributed Outlier Detection in Large Multi-Dimensional Datasets Journal of Computer Science and Technology,2015,30(6):1233-1248
CSCD被引 0 次

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