Autonomous data-driven clustering for live data stream

Xiaowei Gu, Plamen Parvanov Angelov

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

8 Dyfyniadau (Scopus)

Crynodeb

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.

Iaith wreiddiolSaesneg
Teitl2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)
Is-deitlConference Proceedings
CyhoeddwrIEEE Press
Tudalennau1128-1135
Nifer y tudalennau8
ISBN (Electronig)9781509018970
ISBN (Argraffiad)9781509018987
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 09 Chwef 2017
Cyhoeddwyd yn allanolIe

Cyfres gyhoeddiadau

Enw2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

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