An evolving approach to data streams clustering based on typicality and eccentricity data analytics

Bezerra, C.G. and Costa, B.S.J. and Guedes, L.A. and Angelov, P.P. (2020) An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences, 518. pp. 13-28. ISSN 0020-0255

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Abstract

In this paper we propose an algorithm for online clustering of data streams. This algorithm is called AutoCloud and is based on the recently introduced concept of Typicality and Eccentricity Data Analytics, mainly used for anomaly detection tasks. AutoCloud is an evolving, online and recursive technique that does not need training or prior knowledge about the data set. Thus, AutoCloud is fully online, requiring no offline processing. It allows creation and merging of clusters autonomously as new data observations become available. The clusters created by AutoCloud are called data clouds, which are structures without pre-defined shape or boundaries. AutoCloud allows each data sample to belong to multiple data clouds simultaneously using fuzzy concepts. AutoCloud is also able to handle concept drift and concept evolution, which are problems that are inherent in data streams in general. Since the algorithm is recursive and online, it is suitable for applications that require a real-time response. We validate our proposal with applications to multiple well known data sets in the literature.

Item Type:

Journal Article

Journal or Publication Title:

Information Sciences

Additional Information:

This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 518, 2020 DOI: 10.1016/j.ins.2019.12.022

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/1700/1702

Subjects:

?? online clusteringdata streameccentricitytypicalityanomaly detectionartificial intelligencetheoretical computer sciencesoftwareinformation systems and managementcontrol and systems engineeringcomputer science applications ??

Departments:

Faculty of Science and Technology > School of Computing & Communications

ID Code:

140670

Deposited By:

ep_importer_pure

Deposited On:

05 Feb 2020 14:45

Refereed?:

Yes

Published?:

Published