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Fast Algorithm for Finding True Number of Clusters. Applications to Control Systems. / Morozkov, Mikhail; Granichin, Oleg; Volkovich, Zeev; Zhang, Xuming.

PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC). IEEE Canada, 2012. p. 2001-2006 (Chinese Control and Decision Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Morozkov, M, Granichin, O, Volkovich, Z & Zhang, X 2012, Fast Algorithm for Finding True Number of Clusters. Applications to Control Systems. in PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC). Chinese Control and Decision Conference, IEEE Canada, pp. 2001-2006, 24th Chinese Control and Decision Conference (CCDC), Taiyuan, 23/05/12. https://doi.org/10.1109/CCDC.2012.6244322

APA

Morozkov, M., Granichin, O., Volkovich, Z., & Zhang, X. (2012). Fast Algorithm for Finding True Number of Clusters. Applications to Control Systems. In PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) (pp. 2001-2006). (Chinese Control and Decision Conference). IEEE Canada. https://doi.org/10.1109/CCDC.2012.6244322

Vancouver

Morozkov M, Granichin O, Volkovich Z, Zhang X. Fast Algorithm for Finding True Number of Clusters. Applications to Control Systems. In PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC). IEEE Canada. 2012. p. 2001-2006. (Chinese Control and Decision Conference). https://doi.org/10.1109/CCDC.2012.6244322

Author

Morozkov, Mikhail ; Granichin, Oleg ; Volkovich, Zeev ; Zhang, Xuming. / Fast Algorithm for Finding True Number of Clusters. Applications to Control Systems. PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC). IEEE Canada, 2012. pp. 2001-2006 (Chinese Control and Decision Conference).

BibTeX

@inproceedings{4995a9bbf7484bd1b7675ff4a9b73e30,
title = "Fast Algorithm for Finding True Number of Clusters. Applications to Control Systems",
abstract = "One of the most difficult problems in cluster analysis is the identification of the number of groups in a given data set. In this paper we offer the randomized approach in the rate distortion framework. A randomized algorithm has been suggested to allocate this position. The scenario approach is used to significantly reduce the computational complexity. With ability to determine the true number of clusters and perform clustering in real-time operational mode we outline several applications in control systems and decision-making problems that can benefit from algorithm in question essentially. We also provide simulation results to show considerable speed optimization with guaranteed level of probability.",
keywords = "Clustering, Randomized Algorithms, Adaptive Control, Optimitzaion",
author = "Mikhail Morozkov and Oleg Granichin and Zeev Volkovich and Xuming Zhang",
year = "2012",
doi = "10.1109/CCDC.2012.6244322",
language = "Английский",
series = "Chinese Control and Decision Conference",
publisher = "IEEE Canada",
pages = "2001--2006",
booktitle = "PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)",
address = "Канада",
note = "null ; Conference date: 23-05-2012 Through 25-05-2012",

}

RIS

TY - GEN

T1 - Fast Algorithm for Finding True Number of Clusters. Applications to Control Systems

AU - Morozkov, Mikhail

AU - Granichin, Oleg

AU - Volkovich, Zeev

AU - Zhang, Xuming

PY - 2012

Y1 - 2012

N2 - One of the most difficult problems in cluster analysis is the identification of the number of groups in a given data set. In this paper we offer the randomized approach in the rate distortion framework. A randomized algorithm has been suggested to allocate this position. The scenario approach is used to significantly reduce the computational complexity. With ability to determine the true number of clusters and perform clustering in real-time operational mode we outline several applications in control systems and decision-making problems that can benefit from algorithm in question essentially. We also provide simulation results to show considerable speed optimization with guaranteed level of probability.

AB - One of the most difficult problems in cluster analysis is the identification of the number of groups in a given data set. In this paper we offer the randomized approach in the rate distortion framework. A randomized algorithm has been suggested to allocate this position. The scenario approach is used to significantly reduce the computational complexity. With ability to determine the true number of clusters and perform clustering in real-time operational mode we outline several applications in control systems and decision-making problems that can benefit from algorithm in question essentially. We also provide simulation results to show considerable speed optimization with guaranteed level of probability.

KW - Clustering

KW - Randomized Algorithms

KW - Adaptive Control

KW - Optimitzaion

U2 - 10.1109/CCDC.2012.6244322

DO - 10.1109/CCDC.2012.6244322

M3 - статья в сборнике материалов конференции

T3 - Chinese Control and Decision Conference

SP - 2001

EP - 2006

BT - PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)

PB - IEEE Canada

Y2 - 23 May 2012 through 25 May 2012

ER -

ID: 74015784