Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
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 конференций › статья в сборнике материалов конференции › Рецензирование
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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