A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

Yeoh, Jia Ming; Caraffini, Fabio; Homapour, Elmina; Santucci, Valentino; Milani, Alfredo

doi:10.3390/math7121229

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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

Jia Ming Yeoh, Fabio Caraffini

, Elmina Homapour

, Valentino Santucci

, Alfredo Milani

Mathematics, Volume: 7, Issue: 12, Start page: 1229

Swansea University Author: Fabio Caraffini

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DOI (Published version): 10.3390/math7121229

Abstract

This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in...

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Published in:	Mathematics
ISSN:	2227-7390
Published:	MDPI AG 2019
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa60941

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spelling	2022-09-21T15:04:26.8828650 v2 60941 2022-08-28 A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 MACS This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes Journal Article Mathematics 7 12 1229 MDPI AG 2227-7390 dynamic stream clustering; online clustering; metaheuristics; optimisation; population based algorithms; density based clustering; k-means centroid; concept drift; concept evolution 12 12 2019 2019-12-12 10.3390/math7121229 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This research received no external funding. 2022-09-21T15:04:26.8828650 2022-08-28T20:15:02.0058430 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jia Ming Yeoh 1 Fabio Caraffini 0000-0001-9199-7368 2 Elmina Homapour 0000-0001-9756-2744 3 Valentino Santucci 0000-0003-1483-7998 4 Alfredo Milani 0000-0003-4534-1805 5 60941__25187__d36c9908c994446a839861289a4da2b6.pdf 60941_VoR.pdf 2022-09-21T15:03:22.0105657 Output 504942 application/pdf Version of Record true Copyright: 2019 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng http://creativecommons.org/licenses/by/4.0/
title	A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
spellingShingle	A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation Fabio Caraffini
title_short	A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
title_full	A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
title_fullStr	A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
title_full_unstemmed	A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
title_sort	A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
author_id_str_mv	d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv	d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author	Fabio Caraffini
author2	Jia Ming Yeoh Fabio Caraffini Elmina Homapour Valentino Santucci Alfredo Milani
format	Journal article
container_title	Mathematics
container_volume	7
container_issue	12
container_start_page	1229
publishDate	2019
institution	Swansea University
issn	2227-7390
doi_str_mv	10.3390/math7121229
publisher	MDPI AG
college_str	Faculty of Science and Engineering
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hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str	1
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description	This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes
published_date	2019-12-12T14:17:15Z
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score	11.048149

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

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