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	INOD: A graph-based outlier detection algorithm
	Yang, Li Hua ; Li, Gui Lin ; Zhou, Shao Bin ; Liao, Ming Hong ; Li GL(李贵林) ; Liao MH(廖明宏)
	2014
关键词	Algorithms Data mining Graphic methods Sensors Signal detection
英文摘要	Conference Name:2013 2nd International Conference on Sensors, Measurement and lntelligent Materials, ICSMIM 2013. Conference Address: Guangzhou, China. Time:November 16, 2013 - November 17, 2013.; The outlier detection is to select uncommon data from a data set, which can significantly improve the quality of results for the data mining algorithms. A typical feature of the outliers is that they are always far away from a majority of data in the data set. In this paper, we present a graph-based outlier detection algorithm named INOD, which makes use of this feature of the outlier. The DistMean-neighborhood is used to calculate the cumulative in-degree for each data. The data, whose cumulative in-degree is smaller than a threshold, is judged as an outlier candidate. A KNN-based selection algorithm is used to determine the final outlier. Experimental results show that the INOD algorithm can improve the precision 80% higher and decrease the error rate 75% lower than the classical LOF algorithm. ? (2014) Trans Tech Publications, Switzerland.
语种	英语
出处	http://dx.doi.org/10.4028/www.scientific.net/AMM.475-476.1008
出版者	TRANS TECH PUBLICATIONS LTD
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/85813]
专题	软件学院－会议论文
推荐引用方式 GB/T 7714	Yang, Li Hua,Li, Gui Lin,Zhou, Shao Bin,et al. INOD: A graph-based outlier detection algorithm. 2014-01-01.

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