NDLI: Bayesian Network-based detection and prediction of outliers in subspace

Content Provider	IEEE Xplore Digital Library
Author	Lihua Zhou Weiyi Liu Hongmei Chen Lizhen Wang Jilong Chen Xiaodong Yang
Copyright Year	2008
Description	Author affiliation: Sch. of Inf., Yunnan Univ., Kunming (Lihua Zhou; Weiyi Liu; Hongmei Chen; Lizhen Wang; Jilong Chen; Xiaodong Yang)
Abstract	Outlier detection in large data sets is an effective way to find new knowledge and new classes. It has many applications in all those domains that can lead to illegal or abnormal behavior, such as fraud detection and medical diagnosis. Present algorithms mainly focus on finding outliers in data set on the basis of all attributes of data. The concept of outlier in subspace that assumes outlier is only relative to some attributes instead of all attributes is proposed and the relationship between outlier in subspace and in full space is discussed. A subspace outlier, an event which has small probability in data set can be found and a new data can be predicted by using inference ability of Bayesian network based on the relationship between outlier and Bayesian network. A subspace outlier has clear significance which can be explained easily.
Starting Page	2479
Ending Page	2485
File Size	265587
Page Count	7
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593313
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Knowledge engineering Rain Electronic mail Intelligent control Cancer Medical diagnosis Subspace Data Mining Bayesian Network Outlier
Content Type	Text
Resource Type	Article

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