Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library, 155)
Language: English
Published by Springer, 2019
Series: Book 126 of 188 - Intelligent Systems Reference Library
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Title
- Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library, 155)
- Author
- Ranga Suri, N. N. R.; Murty M, Narasimha; Athithan, G.
- Publisher
- Springer
- Publication year
- 2019
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3030051250
- ISBN 13
- 9783030051259
- Item weight
- 633 grams
- Series
- Book 126 of 188: Intelligent Systems Reference Library
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
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