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Outlier Detection: Techniques and Applications = A Data Mining Perspective /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Outlier Detection: Techniques and Applications/ by N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan.
Reminder of title:	A Data Mining Perspective /
Author:	Ranga Suri, N. N. R.
other author:	Murty M, Narasimha.
Description:	XXII, 214 p. 48 illus., 3 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data-driven Science, Modeling and Theory Building. -
Online resource:	https://doi.org/10.1007/978-3-030-05127-3
ISBN:	9783030051273

Outlier Detection: Techniques and Applications = A Data Mining Perspective /
Ranga Suri, N. N. R.

Outlier Detection: Techniques and ApplicationsA Data Mining Perspective /[electronic resource] :by N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan. - 1st ed. 2019. - XXII, 214 p. 48 illus., 3 illus. in color.online resource. - Intelligent Systems Reference Library,1551868-4394 ;. - Intelligent Systems Reference Library,67.

Introduction -- Outlier Detection -- Research Issues in Outlier Detection -- Computational Preliminaries -- Outlier Detection in Categorical Data -- Outliers in High Dimensional Data.

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. .

ISBN: 9783030051273

Standard No.: 10.1007/978-3-030-05127-3doiSubjects--Topical Terms:

1112983
Data-driven Science, Modeling and Theory Building.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Outlier Detection: Techniques and Applications = A Data Mining Perspective /
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