Name: Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library) (en Inglés)
SKU: 51193432
Price: 178.40 USD
Availability: InStock
Author: Ranga Suri,Murty M,Athithan
ISBN: 9783030051259

portada Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library) (en Inglés)

Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Ranga Suri,Murty M,Athithan

Año

2019

Idioma

Inglés

N° páginas

214

Encuadernación

Tapa Dura

ISBN13

9783030051259

N° edición

2019

Categorías

Inteligencia artificial

Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library) (en Inglés)

Ranga Suri,Murty M,Athithan (Autor) · Springer · Tapa Dura

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Reseña del libro "Outlier Detection: Techniques and Applications: A Data Mining Perspective (Intelligent Systems Reference Library) (en Inglés)"

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.