Outlier Detection: Techniques and Applications

N. N. R. Ranga Suri

ISBN 10: 3030051250 / ISBN 13: 9783030051259

Editore: Springer International Publishing, Springer Nature Switzerland Jan 2019, 2019

Riguardo questo articolo

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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 236 pp. Englisch.

Codice articolo 9783030051259

Dati bibliografici

• Titolo: Outlier Detection: Techniques and Applications
• Editore: Springer International Publishing, Springer Nature Switzerland Jan 2019
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030051250
• ISBN 13: 9783030051259
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 19 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 19 millimetri di profondità
• Peso: 524 grammi

Descrizione articolo

Riassunto

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.