Information-Theoretic Outlier Detection for Large-Scale Categorical Data

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	Information-Theoretic Outlier Detection for Large-Scale Categorical Data
	Wu, Shu1 ; Wang, Shengrui 2
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2013-03-01
卷号	25 期号:3 页码:589-602
文章类型	Article
摘要	Outlier detection can usually be considered as a pre-processing step for locating, in a data set, those objects that do not conform to well-defined notions of expected behavior. It is very important in data mining for discovering novel or rare events, anomalies, vicious actions, exceptional phenomena, etc. We are investigating outlier detection for categorical data sets. This problem is especially challenging because of the difficulty of defining a meaningful similarity measure for categorical data. In this paper, we propose a formal definition of outliers and an optimization model of outlier detection, via a new concept of holoentropy that takes both entropy and total correlation into consideration. Based on this model, we define a function for the outlier factor of an object which is solely determined by the object itself and can be updated efficiently. We propose two practical 1-parameter outlier detection methods, named ITB-SS and ITB-SP, which require no user-defined parameters for deciding whether an object is an outlier. Users need only provide the number of outliers they want to detect. Experimental results show that ITB-SS and ITB-SP are more effective and efficient than mainstream methods and can be used to deal with both large and high-dimensional data sets where existing algorithms fail.
关键词	Outlier Detection Holoentropy Total Correlation Outlier Factor Attribute Weighting Greedy Algorithms
WOS标题词	Science & Technology ; Technology
关键词[WOS]	ANOMALY DETECTION ; LOCAL OUTLIERS ; DATA SETS ; SUPPORT
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000314934900009
引用统计	被引频次：75[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/10749
专题	模式识别实验室
通讯作者	Wu, Shu
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 2.Univ Sherbrooke, Dept Comp Sci, Sherbrooke, PQ J1K 2R1, Canada
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Wu, Shu,Wang, Shengrui. Information-Theoretic Outlier Detection for Large-Scale Categorical Data[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2013,25(3):589-602.
APA	Wu, Shu,&Wang, Shengrui.(2013).Information-Theoretic Outlier Detection for Large-Scale Categorical Data.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,25(3),589-602.
MLA	Wu, Shu,et al."Information-Theoretic Outlier Detection for Large-Scale Categorical Data".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 25.3(2013):589-602.

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