An adaptive algorithm for anomaly and novelty detection in evolving data streams

In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribut...

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Bibliographic Details
Published in:	Data mining and knowledge discovery Vol. 32; no. 6; pp. 1597 - 1633
Main Authors:	Bouguelia, Mohamed-Rafik, Nowaczyk, Slawomir, Payberah, Amir H.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2018 Springer Nature B.V
Subjects:	Adaptive algorithms Adaptive systems Algorithms Anomaly and novelty detection Artificial Intelligence Big data Change detection Chemistry and Earth Sciences Computer Science Data management Data mining Data Mining and Knowledge Discovery Data stream Data transmission Evolution Growing neural gas Information Storage and Retrieval Intelligent systems Machine learning Neurons Non-stationary environment Non-stationary environments Novelty detection Physics Statistics for Engineering Non-stationary environments Data stream Growing neural gas Anomaly and novelty detection Change detection
ISSN:	1384-5810, 1573-756X, 1573-756X
Online Access:	Get full text
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