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Outlier Detection: Techniques and Applications: A Data Mining Perspective: 155 (Intelligent Systems Reference Library, 155) - Hardcover
Synopsis
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.
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From the Back Cover
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.
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Outlier Detection: Techniques and Applications: A Data Mining Perspective: 155 (Intelligent Systems Reference Library, 155) [Hardcover] Ranga Suri, N. N. R.; Murty M, Narasimha and Athithan, G.
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Outlier Detection: Techniques and Applications : A Data Mining Perspective
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Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library, 155)
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Outlier Detection: Techniques and Applications
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Springer International Publishing, 2019
ISBN 10: 3030051250
ISBN 13: 9783030051259
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a comprehensive survey of the outlier detection problem including a list of issues, challenges and relevant literaturePresents the latest methods for outlier detection with a special focus on categorical data Employs outlier. Seller Inventory # 257249224
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Outlier Detection: Techniques and Applications : A Data Mining Perspective
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Outlier Detection: Techniques and Applications
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Springer International Publishing, Springer Nature Switzerland Jan 2019, 2019
ISBN 10: 3030051250
ISBN 13: 9783030051259
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 236 pp. Englisch. Seller Inventory # 9783030051259
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Outlier Detection: Techniques and Applications : A Data Mining Perspective
Published by
Springer International Publishing, Springer Nature Switzerland, 2019
ISBN 10: 3030051250
ISBN 13: 9783030051259
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783030051259
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Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library, 155)
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