Strucutural Comparison and Cluster Analysis of Time-Series Medical Data

2005 ◽  
Author(s):  
S. Hirano ◽  
S. Tsumoto
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Medical Data ◽  
And Cluster Analysis ◽  
Analysis Of Time Series

scholarly journals Stages of spatial dispersion of the COVID-19 epidemic in Poland in the first six months (4 March-20 September, 2020).

2021 ◽  
Vol 94 (3) ◽  
pp. 305-324
Author(s):  
Przemysław Śleszyński
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Spatial Distribution ◽  
Population Density ◽  
Spatial Patterns ◽  
Concentration Index ◽  
Spatial Dispersion ◽  
First Case ◽  
And Cluster Analysis ◽  
Day By Day

The article is a continuation of research published by the author elsewhere (Śleszyński, 2020). The elaboration presents the regularity of spatial distribution of infections during the first six months after the detection of SARS-CoV-2 coronovirus in Poland under strong lockdown conditions. The main aim is to try to determine the basic temporal-spatial patterns and to answer the questions: to what extent the phenomenon was ordered and to what extent it was chaotic, whether there are any particular features of spread, whether the infection is concentrated or dispersed and whether the spreading factors in Poland are similar to those observed in other countries. Day by day data were used according to the counties collected in Rogalski’s team (2020). The data were aggregated to weekly periods (7 days) and then the regularity of spatial distribution was searched for using the cartogram method, time series shifts, rope correlation between the intensity of infections in different periods, Herfindahl-Hirschman concentration index (HHI) and cluster analysis. A spatial typology of infection development in the population was also performed. Among other things, it was shown that during the first period (about 100 days after the first case), the infections became more and more spatially concentrated and then dispersed. Differences were also shown in relation to the spread of the infection compared to observations from other countries, i.e. no relation to population density and level of urbanization.

scholarly journals Cluster analysis of time series K-Means line loss based on DWT distance

2021 ◽  
Vol 2137 (1) ◽  
pp. 012063
Author(s):  
Liming Song ◽  
Zhimin Chen ◽  
XinXin Meng ◽  
Shuai Kang
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Indicator System ◽  
Analysis Model ◽  
Running Time ◽  
Loss Analysis ◽  
Model Based ◽  
Traditional Algorithm ◽  
Line Loss ◽  
Analysis Of Time Series

Abstract This paper constructs an indicator system composed of inherent attributes and time characteristics of the line based on the line loss, and proposes a K-Means line loss cluster analysis model based on this indicator system. The line is classified according to the clustering results. The result is 314.51 on the CH index (Calinski Harabasz Index), 0.19 on the Silhouette Cofficient (Silhouette Cofficient), and a running time of 0.508s. Compared with the traditional algorithm, it is greatly improved. The field of line loss analysis has guiding significance.

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