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  • Paper_Information_Sciences_Revised auto-cloud 2020

    Rights statement: 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

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

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>31/05/2020
<mark>Journal</mark>Information Sciences
Volume518
Number of pages16
Pages (from-to)13-28
Publication StatusPublished
Early online date2/01/20
<mark>Original language</mark>English

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.

Bibliographic note

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