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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Autonomous data-driven clustering for live data stream
AU - Gu, Xiaowei
AU - Angelov, Plamen Parvanov
PY - 2016/10/9
Y1 - 2016/10/9
N2 - 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, discards all the previous data samples. Experimental results based on benchmark datasets exhibit the higher performance of the proposed fully autonomous approach compared with the comparative approaches with 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.
AB - 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, discards all the previous data samples. Experimental results based on benchmark datasets exhibit the higher performance of the proposed fully autonomous approach compared with the comparative approaches with 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.
KW - fully unsupervised clustering
KW - live data streams
KW - ensemble properties
KW - recursive update
KW - streaming data analytics
U2 - 10.1109/SMC.2016.7844394
DO - 10.1109/SMC.2016.7844394
M3 - Conference contribution/Paper
SN - 9781509018987
SP - 1128
EP - 1135
BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)
PB - IEEE
T2 - 2016 IEEE SMC
Y2 - 9 October 2016
ER -