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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

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

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

Swansea University Author: Fabio Caraffini Orcid Logo

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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa60941
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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 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
Keywords: dynamic stream clustering; online clustering; metaheuristics; optimisation; population based algorithms; density based clustering; k-means centroid; concept drift; concept evolution
College: College of Science
Funders: This research received no external funding.
Issue: 12
Start Page: 1229