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Methods of cluster analysis for detection of homogeneous groups of healthcare time series. / Bure, Vladimir; Staroverova, Kseniya.

2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings. ред. / LN Polyakova. Institute of Electrical and Electronics Engineers Inc., 2017. стр. 61-64 7973944.

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаяРецензирование

Harvard

Bure, V & Staroverova, K 2017, Methods of cluster analysis for detection of homogeneous groups of healthcare time series. в LN Polyakova (ред.), 2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings., 7973944, Institute of Electrical and Electronics Engineers Inc., стр. 61-64, Международная конференция «Конструктивный негладкий анализ и смежные вопросы», Saint-Petersburg, Российская Федерация, 22/05/17. https://doi.org/10.1109/CNSA.2017.7973944

APA

Bure, V., & Staroverova, K. (2017). Methods of cluster analysis for detection of homogeneous groups of healthcare time series. в LN. Polyakova (Ред.), 2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings (стр. 61-64). [7973944] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CNSA.2017.7973944

Vancouver

Bure V, Staroverova K. Methods of cluster analysis for detection of homogeneous groups of healthcare time series. в Polyakova LN, Редактор, 2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. стр. 61-64. 7973944 https://doi.org/10.1109/CNSA.2017.7973944

Author

Bure, Vladimir ; Staroverova, Kseniya. / Methods of cluster analysis for detection of homogeneous groups of healthcare time series. 2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings. Редактор / LN Polyakova. Institute of Electrical and Electronics Engineers Inc., 2017. стр. 61-64

BibTeX

@inproceedings{79ddb03adefb4ce194a2dee2e8415326,
title = "Methods of cluster analysis for detection of homogeneous groups of healthcare time series",
abstract = "Statistical analysis is widely used for problem solving in different fields. We present a research on Saint Petersburg morbidity rate. The aim of the work is to detect the heterogeneity in districts of the city with respect to morbidity rate, which was chosen as an indicator of population health. Methods of cluster analysis was utilized for grouping districts to homogeneous sets. Clustering can be considered as an optimization problem as the distance between elements from the same group must be as little as possible, at the same time the distance between elements from different clusters must be as great as possible. Key feature of the research is that data are time dependent so it is necessary to use special dissimilarity measures. Besides each district is characterized by three values: children, teenagers and adult morbidity that call for multidimensional time series analysis. Firstly, a multidimensional clustering analysis was made. Then we conduct the analysis of children morbidity rate and propose a new dissimilarity measure for short time series.",
author = "Vladimir Bure and Kseniya Staroverova",
year = "2017",
month = jul,
day = "10",
doi = "10.1109/CNSA.2017.7973944",
language = "English",
pages = "61--64",
editor = "LN Polyakova",
booktitle = "2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2017 Constructive Nonsmooth Analysis and Related Topics : dedicated to the Memory of V.F. Demyanov, CNSA 2017 ; Conference date: 22-05-2017 Through 27-05-2017",
url = "http://www.mathnet.ru/php/conference.phtml?confid=968&option_lang=rus, http://www.pdmi.ras.ru/EIMI/2017/CNSA/",

}

RIS

TY - GEN

T1 - Methods of cluster analysis for detection of homogeneous groups of healthcare time series

AU - Bure, Vladimir

AU - Staroverova, Kseniya

PY - 2017/7/10

Y1 - 2017/7/10

N2 - Statistical analysis is widely used for problem solving in different fields. We present a research on Saint Petersburg morbidity rate. The aim of the work is to detect the heterogeneity in districts of the city with respect to morbidity rate, which was chosen as an indicator of population health. Methods of cluster analysis was utilized for grouping districts to homogeneous sets. Clustering can be considered as an optimization problem as the distance between elements from the same group must be as little as possible, at the same time the distance between elements from different clusters must be as great as possible. Key feature of the research is that data are time dependent so it is necessary to use special dissimilarity measures. Besides each district is characterized by three values: children, teenagers and adult morbidity that call for multidimensional time series analysis. Firstly, a multidimensional clustering analysis was made. Then we conduct the analysis of children morbidity rate and propose a new dissimilarity measure for short time series.

AB - Statistical analysis is widely used for problem solving in different fields. We present a research on Saint Petersburg morbidity rate. The aim of the work is to detect the heterogeneity in districts of the city with respect to morbidity rate, which was chosen as an indicator of population health. Methods of cluster analysis was utilized for grouping districts to homogeneous sets. Clustering can be considered as an optimization problem as the distance between elements from the same group must be as little as possible, at the same time the distance between elements from different clusters must be as great as possible. Key feature of the research is that data are time dependent so it is necessary to use special dissimilarity measures. Besides each district is characterized by three values: children, teenagers and adult morbidity that call for multidimensional time series analysis. Firstly, a multidimensional clustering analysis was made. Then we conduct the analysis of children morbidity rate and propose a new dissimilarity measure for short time series.

UR - http://www.scopus.com/inward/record.url?scp=85027445729&partnerID=8YFLogxK

U2 - 10.1109/CNSA.2017.7973944

DO - 10.1109/CNSA.2017.7973944

M3 - Conference contribution

AN - SCOPUS:85027445729

SP - 61

EP - 64

BT - 2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings

A2 - Polyakova, LN

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 Constructive Nonsmooth Analysis and Related Topics

Y2 - 22 May 2017 through 27 May 2017

ER -

ID: 33148173