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Outlier detectiontechniques and appl...
~
Athithan, G.
Outlier detectiontechniques and applications : a data mining perspective /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Outlier detectionby N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan.
Reminder of title:
techniques and applications : a data mining perspective /
Author:
Ranga Suri, N. N. R.
other author:
Murty, M Narasimha.
Published:
Cham :Springer International Publishing :2019.
Description:
xxii, 214 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Data mining.
Online resource:
https://doi.org/10.1007/978-3-030-05127-3
ISBN:
9783030051273$q(electronic bk.)
Outlier detectiontechniques and applications : a data mining perspective /
Ranga Suri, N. N. R.
Outlier detection
techniques and applications : a data mining perspective /[electronic resource] :by N. N. R. Ranga Suri, Narasimha Murty M, G. Athithan. - Cham :Springer International Publishing :2019. - xxii, 214 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1551868-4394 ;. - Intelligent systems reference library ;v.24..
Introduction -- Outlier Detection -- Research Issues in Outlier Detection -- Computational Preliminaries -- Outlier Detection in Categorical Data -- Outliers in High Dimensional Data.
This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.
ISBN: 9783030051273$q(electronic bk.)
Standard No.: 10.1007/978-3-030-05127-3doiSubjects--Topical Terms:
184440
Data mining.
LC Class. No.: QA76.9.D343 / R364 2019
Dewey Class. No.: 006.312
Outlier detectiontechniques and applications : a data mining perspective /
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Introduction -- Outlier Detection -- Research Issues in Outlier Detection -- Computational Preliminaries -- Outlier Detection in Categorical Data -- Outliers in High Dimensional Data.
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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.
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Intelligent Technologies and Robotics (Springer-42732)
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000000168460
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EB QA76.9.D343 R196 2019 2019
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https://doi.org/10.1007/978-3-030-05127-3
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