Cluster Analysis of Time-Series Medical Data Based on the Trajectory Representation and Multiscale Comparison Techniques

2006 ◽  
Author(s):  
Shoji Hirano ◽  
Shusaku Tsumoto
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Medical Data ◽  
Trajectory Representation ◽  
Analysis Of Time Series

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.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

Sign in / Sign up

Export Citation Format

Share Document