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Autonomous data-driven clustering for live data stream

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Autonomous data-driven clustering for live data stream. / Gu, Xiaowei; Angelov, Plamen Parvanov.

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). IEEE, 2016. p. 1128-1135 1303.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Gu, X & Angelov, PP 2016, Autonomous data-driven clustering for live data stream. in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)., 1303, IEEE, pp. 1128-1135, 2016 IEEE SMC, 9/10/16. https://doi.org/10.1109/SMC.2016.7844394

APA

Gu, X., & Angelov, P. P. (2016). Autonomous data-driven clustering for live data stream. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016) (pp. 1128-1135). [1303] IEEE. https://doi.org/10.1109/SMC.2016.7844394

Vancouver

Gu X, Angelov PP. Autonomous data-driven clustering for live data stream. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). IEEE. 2016. p. 1128-1135. 1303 https://doi.org/10.1109/SMC.2016.7844394

Author

Gu, Xiaowei ; Angelov, Plamen Parvanov. / Autonomous data-driven clustering for live data stream. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). IEEE, 2016. pp. 1128-1135

Bibtex

@inproceedings{af309e0ee6da47daac34490c80e74c57,
title = "Autonomous data-driven clustering for live data stream",
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, 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.",
keywords = "fully unsupervised clustering, live data streams, ensemble properties, recursive update, streaming data analytics",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
year = "2016",
month = "10",
day = "9",
doi = "10.1109/SMC.2016.7844394",
language = "English",
isbn = "9781509018987",
pages = "1128--1135",
booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)",
publisher = "IEEE",

}

RIS

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

ER -