Abstract
Physical exercise physiological index monitoring has a wide range of applications in the fields of physiological index planning and design and organizational network evolution. Among the existing analysis methods for monitoring data points of physical exercise physiological indicators, the analysis error of point events under linear constraints is relatively large. Based on discrete data-driven datasets, this paper realizes the monitoring and visualization of sports physiological indicators. First, the principal component analysis of multivariate discrete data is used for dimensionality reduction. Second, the clustering of discrete physical exercise data uses the BIC criterion to preset the number of clusters, and the R software is used to visually realize the clustering results of physical exercise physiological indicators in each region in the text. The experiment solves the problem of mismatch of model parameter combinations when the physical exercise index monitoring quantity is used for the auxiliary analysis of the clustering results. Through the ARI index monitoring, the accuracy of the clustering physical exercise results of the method is increased to 89.7%, and the error rate is controlled within 4.3%. It promotes the superiority and effectiveness of multivariate discrete data-driven model clustering methods.
Highlights
The B-spline basis function is selected to smoothly fit the original discrete data, and the functional dimension reduction is performed on the monthly average concentration curve based on the functional principal component analysis, and the functional principal component score matrix is obtained. e multivariate function Gaussian mixture model and the multivariate function K-means method are used to perform cluster analysis on the data quality of the country and region, and, respectively, describe the spatiotemporal characteristics of different physiological indicators affecting the data quality in different regions of the country in the past three years, and use R software to realize the type of clustering results intuitively in the text [9–11]
In order to test the influence of sports monitoring on the global physiological index error stress in the discrete data algorithm, in this experiment, we randomly generated 9 correlation coefficient matrices according to its method
The discrete data dimension correlation matrix is first established by the physical exercise data correlation coefficient, and the discrete data dimension correlation matrix is transformed into the physical exercise data dimension on the plane through the transformation function
Summary
In terms of discrete data processing, regression analysis uses mathematical statistics to reveal the interdependence (correlation) between two or more dimensions. If there is such a relationship, when visualizing, all samples will be in a certain, and if this relationship does not exist, the visualization effect is a group of discrete points [1]. E multivariate function Gaussian mixture model and the multivariate function K-means method are used to perform cluster analysis on the data quality of the country and region, and, respectively, describe the spatiotemporal characteristics of different physiological indicators affecting the data quality in different regions of the country in the past three years, and use R software to realize the type of clustering results intuitively in the text [9–11]. We provide relevant decision-making basis and theoretical support for future environmental governance, so as to monitor the data quality status and guide useful information in the future
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