Overview
- 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 on categorical data
- Employs outlier detection principles in contemporary applications such as anomaly detection in network data and characterizing temporal anomalies/outliers in dynamic social networks
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 155)
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Table of contents (11 chapters)
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Front Matter
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Techniques for Outlier Detection
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Front Matter
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Applications of Outlier Detection in Graph Data Mining
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Outlier Detection: Techniques and Applications
Book Subtitle: A Data Mining Perspective
Authors: N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-030-05127-3
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-05125-9Published: 24 January 2019
eBook ISBN: 978-3-030-05127-3Published: 10 January 2019
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XXII, 214
Number of Illustrations: 45 b/w illustrations, 3 illustrations in colour
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Data-driven Science, Modeling and Theory Building