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Cluster ensemble construction with the algorithm of averaged centroids. / Tatarnikov, V.; Berikov Sobolev, V.; Pestunov, I.

Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. стр. 342-345 8109902.

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Harvard

Tatarnikov, V, Berikov Sobolev, V & Pestunov, I 2017, Cluster ensemble construction with the algorithm of averaged centroids. в Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017., 8109902, Institute of Electrical and Electronics Engineers Inc., стр. 342-345, 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017, Novosibirsk, Российская Федерация, 18.09.2017. https://doi.org/10.1109/SIBIRCON.2017.8109902

APA

Tatarnikov, V., Berikov Sobolev, V., & Pestunov, I. (2017). Cluster ensemble construction with the algorithm of averaged centroids. в Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017 (стр. 342-345). [8109902] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON.2017.8109902

Vancouver

Tatarnikov V, Berikov Sobolev V, Pestunov I. Cluster ensemble construction with the algorithm of averaged centroids. в Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. Institute of Electrical and Electronics Engineers Inc. 2017. стр. 342-345. 8109902 doi: 10.1109/SIBIRCON.2017.8109902

Author

Tatarnikov, V. ; Berikov Sobolev, V. ; Pestunov, I. / Cluster ensemble construction with the algorithm of averaged centroids. Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. стр. 342-345

BibTeX

@inproceedings{631216c855674b7e8a83a020983a548e,
title = "Cluster ensemble construction with the algorithm of averaged centroids",
abstract = "The task of finding consensus solution of cluster analysis problem is considered in the paper. A heuristic algorithm for constructing consensus clustering partition using any centroid-based algorithm is proposed. It is theoretically proved that the algorithm is statistically stable. The novelty of the algorithm is that it is implemented and optimized for running in parallel and distributed environment. The paper includes the results of testing the algorithm on artificial and real data.",
keywords = "Centroid, Cluster ensemble, Hyperspectral image analysis, K-means",
author = "V. Tatarnikov and {Berikov Sobolev}, V. and I. Pestunov",
year = "2017",
month = nov,
day = "14",
doi = "10.1109/SIBIRCON.2017.8109902",
language = "English",
pages = "342--345",
booktitle = "Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017 ; Conference date: 18-09-2017 Through 22-09-2017",

}

RIS

TY - GEN

T1 - Cluster ensemble construction with the algorithm of averaged centroids

AU - Tatarnikov, V.

AU - Berikov Sobolev, V.

AU - Pestunov, I.

PY - 2017/11/14

Y1 - 2017/11/14

N2 - The task of finding consensus solution of cluster analysis problem is considered in the paper. A heuristic algorithm for constructing consensus clustering partition using any centroid-based algorithm is proposed. It is theoretically proved that the algorithm is statistically stable. The novelty of the algorithm is that it is implemented and optimized for running in parallel and distributed environment. The paper includes the results of testing the algorithm on artificial and real data.

AB - The task of finding consensus solution of cluster analysis problem is considered in the paper. A heuristic algorithm for constructing consensus clustering partition using any centroid-based algorithm is proposed. It is theoretically proved that the algorithm is statistically stable. The novelty of the algorithm is that it is implemented and optimized for running in parallel and distributed environment. The paper includes the results of testing the algorithm on artificial and real data.

KW - Centroid

KW - Cluster ensemble

KW - Hyperspectral image analysis

KW - K-means

UR - http://www.scopus.com/inward/record.url?scp=85040532588&partnerID=8YFLogxK

U2 - 10.1109/SIBIRCON.2017.8109902

DO - 10.1109/SIBIRCON.2017.8109902

M3 - Conference contribution

AN - SCOPUS:85040532588

SP - 342

EP - 345

BT - Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017

Y2 - 18 September 2017 through 22 September 2017

ER -

ID: 9870062