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Local modes-based free-shape data partitioning

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Local modes-based free-shape data partitioning. / Angelov, Plamen Parvanov; Gu, Xiaowei.
2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016.

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

Harvard

Angelov, PP & Gu, X 2016, Local modes-based free-shape data partitioning. in 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, IEEE SSCI 2016, ATHENS, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7850117

APA

Angelov, P. P., & Gu, X. (2016). Local modes-based free-shape data partitioning. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) IEEE. https://doi.org/10.1109/SSCI.2016.7850117

Vancouver

Angelov PP, Gu X. Local modes-based free-shape data partitioning. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2016 doi: 10.1109/SSCI.2016.7850117

Author

Angelov, Plamen Parvanov ; Gu, Xiaowei. / Local modes-based free-shape data partitioning. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016.

Bibtex

@inproceedings{7f483fe5d6a94718bb227f8f55b9ca31,
title = "Local modes-based free-shape data partitioning",
abstract = "In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms.",
keywords = "data partitioning, evolving clustering, parameter-free, data cloud, data- driven",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu",
year = "2016",
month = dec,
day = "9",
doi = "10.1109/SSCI.2016.7850117",
language = "English",
isbn = "9781509042418",
booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)",
publisher = "IEEE",
note = "IEEE SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

}

RIS

TY - GEN

T1 - Local modes-based free-shape data partitioning

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

PY - 2016/12/9

Y1 - 2016/12/9

N2 - In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms.

AB - In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms.

KW - data partitioning

KW - evolving clustering

KW - parameter-free

KW - data cloud

KW - data- driven

U2 - 10.1109/SSCI.2016.7850117

DO - 10.1109/SSCI.2016.7850117

M3 - Conference contribution/Paper

SN - 9781509042418

BT - 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

PB - IEEE

T2 - IEEE SSCI 2016

Y2 - 6 December 2016 through 9 December 2016

ER -