A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

Volume: 11, Issue: 4, Pages: 1 - 31
Published: Apr 19, 2016
Abstract
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as...
Paper Details
Title
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.
Published Date
Apr 19, 2016
Journal
Volume
11
Issue
4
Pages
1 - 31
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