Information-Theoretic Outlier Detection for Large-Scale Categorical Data
文献类型：期刊论文
| 作者 | Wu, Shu1 ; Wang, Shengrui2
|
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2013-03-01 |
| 卷号 | 25期号:3页码:589-602 |
| 关键词 | Outlier detection holoentropy total correlation outlier factor attribute weighting greedy algorithms |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | ANOMALY DETECTION ; LOCAL OUTLIERS ; DATA SETS ; SUPPORT |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000314934900009 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10749]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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