Outlier Detection: Techniques and Applications: A Data Mining Perspective
Language: English
Published by Springer, 2019
Series: Book 126 of 188 - Intelligent Systems Reference Library
- Hardcover
- New

Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
US$ 317.89
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- Title
- Outlier Detection: Techniques and Applications: A Data Mining Perspective
- Author
- Ranga Suri, N. N. R. (Author)/ Murty M, Narasimha (Author)/ Athithan, G. (Author)
- Publisher
- Springer
- Publication year
- 2019
- Condition
- Brand New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3030051250
- ISBN 13
- 9783030051259
- Item weight
- 0.51 kilograms
- 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.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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