Kishan G. Mehrotra, Chilukuri K. Mohan, Huaming Huang

Anomaly Detection Principles and Algorithms (eBook, PDF)

• Format: PDF

Produktbeschreibung

Presents new algorithms for static and time series datasets

Introduces new ensemble methods for improved anomaly detection

Covers rank-based anomaly detection algorithms

Discusses the pros and cons of various approaches used for anomaly detection

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 217
• Erscheinungstermin: 18. November 2017
• Englisch
• ISBN-13: 9783319675268
• Artikelnr.: 53061120

Inhaltsangabe

1 Introduction.- 2 Anomaly Detection.- 3 Distance-based Anomaly Detection Approaches.- 4 Clustering-based Anomaly Detection Approaches.- 5 Model-based Anomaly Detection Approaches.- 6 Distance and Density Based Approaches.- 7 Rank Based Approaches.- 8 Ensemble Methods.- 9 Algorithms for Time Series Data.- Datasets for Evaluation.- Datasets for Time Series Experiments.

Rezensionen

"This book presents the interesting topic of anomaly detection for a very broad audience. ... The presentation is really useful: for each technique, some motivation is given, including real-life situations, a comprehensible formalization, and pros and cons, which gives readers an idea of how useful the technique will be in practice. ... Probably the most important contribution of the book is its citations and references for further reading, which may help casual readers better understand each technique ... ." (Santiago Escobar, Computing Reviews, January, 2019)