TPICDS: A Two-Phase Parallel Approach for Incremental Clustering of Data Streams

Al Abd Alazeez, Ammar and Jassim, Sabah and Du, Hongbo (2019) TPICDS: A Two-Phase Parallel Approach for Incremental Clustering of Data Streams. In: Euro-Par 2018: Parallel Processing Workshops. Lecture Notes in Computer Science (11339). Springer Cham, pp. 5-16. ISBN 978-3-030-10548-8

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Parallel and distributed solutions are essential for clustering data streams due to the large volumes of data. This paper first examines a direct adaptation of a recently developed prototype-based algorithm into three existing parallel frameworks. Based on the evaluation of performance, the paper then presents a customised pipeline framework that combines incremental and twophase learning into a balanced approach that dynamically allocates the available processing resources. This new framework is evaluated on a collection of synthetic datasets. The experimental results reveal that the framework not only produces correct final clusters on the one hand, but also significantly improves the clustering efficiency

Item Type: Book Section
Additional Information: Proceedings of the Euro-Par 2018 conference held in Turin, Italy, August 27-28, 2018
Uncontrolled Keywords: Big data; Data stream clustering algorithms; Distributed and parallel framework
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Hongbo Du
Date Deposited: 12 Mar 2020 10:35
Last Modified: 19 Feb 2021 01:15

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