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Titlebook: Outlier Detection: Techniques and Applications; A Data Mining Perspe N. N. R. Ranga Suri,Narasimha Murty M,G. Athithan Book 2019 Springer N

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书目名称Outlier Detection: Techniques and Applications
副标题A Data Mining Perspe
编辑N. N. R. Ranga Suri,Narasimha Murty M,G. Athithan
视频video
概述Provides a comprehensive survey of the outlier detection problem including a list of issues, challenges and relevant literature.Presents the latest methods for outlier detection with a special focus o
丛书名称Intelligent Systems Reference Library
图书封面Titlebook: Outlier Detection: Techniques and Applications; A Data Mining Perspe N. N. R. Ranga Suri,Narasimha Murty M,G. Athithan Book 2019 Springer N
描述.This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.   .
出版日期Book 2019
关键词Data Mining; Intelligent Systems; Outlier Detection; Robust Statistics; Rough Sets; data-driven science,
版次1
doihttps://doi.org/10.1007/978-3-030-05127-3
isbn_ebook978-3-030-05127-3Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightSpringer Nature Switzerland AG 2019
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Book 2019al interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in networ
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Springer Nature Switzerland AG 2019
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