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Open AccessArticle

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

1
Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
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Department of Humanities and Social Sciences, University for Foreigners of Perugia, piazza G. Spitella 3, 06123 Perugia, Italy
3
Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli 1, 06123 Perugia, Italy
*
Author to whom correspondence should be addressed.
Mathematics 2019, 7(12), 1229; https://doi.org/10.3390/math7121229
Received: 31 October 2019 / Revised: 9 December 2019 / Accepted: 10 December 2019 / Published: 12 December 2019
(This article belongs to the Special Issue Evolutionary Computation & Swarm Intelligence)
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. View Full-Text
Keywords: dynamic stream clustering; online clustering; metaheuristics; optimisation; population based algorithms; density based clustering; k-means centroid; concept drift; concept evolution dynamic stream clustering; online clustering; metaheuristics; optimisation; population based algorithms; density based clustering; k-means centroid; concept drift; concept evolution
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MDPI and ACS Style

Yeoh, J.M.; Caraffini, F.; Homapour, E.; Santucci, V.; Milani, A. A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation. Mathematics 2019, 7, 1229.

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