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Autonomous data-driven clustering for live data stream

Gu, Xiaowei, Angelov, Plamen P. (2016) Autonomous data-driven clustering for live data stream. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). . 001128-001135. IEEE ISBN 978-1-5090-1898-7. E-ISBN 978-1-5090-1897-0. (doi:10.1109/SMC.2016.7844394) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90214)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/SMC.2016.7844394

Abstract

In this paper, a novel autonomous data-driven clustering approach, called AD_clustering, is presented for live data streams processing. This newly proposed algorithm is a fully unsupervised approach and entirely based on the data samples and their ensemble properties, in the sense that there is no need for user-predefined or problem-specific assumptions and parameters, which is a problem most of the current clustering approaches suffer from. Moreover, the proposed approach automatically evolves its structure according to the experimentally observable streaming data and is able to recursively update its self-defined parameters using only the current data sample; meanwhile, it discards all the previously processed data samples. Experimental results based on benchmark datasets exhibit the higher performance of the proposed fully autonomous approach compared with the comparative approaches requiring user- and problem-specific parameters to be predefined. This new clustering algorithm is a promising tool for further applications in the field of real-time streaming data analytics.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/SMC.2016.7844394
Uncontrolled keywords: Clustering algorithms; Algorithm design and analysis; Chebyshev approximation; Mathematical model; Conferences; Cybernetics; Data analysis; fully unsupervised clustering; live data streams; ensemble properties; recursive update; streaming data analytics
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 14:54 UTC
Last Modified: 15 Sep 2021 14:38 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90214 (The current URI for this page, for reference purposes)
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