Outlier Detection: Techniques and Applications - A Data Mining Perspective (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Intelligent Systems Reference Library, 155
Release date:	2019
First published:	2019
Authors:	N. N. R. Ranga Suri • Narasimha Murty M • G. Athithan
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	214
Edition:	1st ed. 2019
ISBN-13:	978-3-03-005125-9
Categories:	Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-005125-0
Barcode:	9783030051259

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