Research On Multidimensional Data Visualization Method Based On Parallel Coordinates And Radial Coordinates

The world today has entered the era of big data.Faced with massive data,how to detect the effective information is a key issue that needs to be solved urgently.Data visualization is an interdisciplinary subject covering computer graphics,data science,natural sciences,and human-computer interaction.It can map raw abstract data into a visual structure,which is an indispensable tool in data analysis.Parallel coordinates and radial coordinate visualization are two common techniques for visualizing high-dimensional data.They can map high-dimensional data in two-dimensional space,which facilitates the intuitive analysis and research of data.However,the existing visualization technology still has certain limitations,and the data overlap phenomenon is more common,which greatly affects the data extraction capability of the visualization technology.Therefore,this paper studies the problem of multidimensional data visualization with parallel coordinates and radial coordinates as the framework,aiming at improving the shortcomings of current visualization technology and improving the ability of existing technologies to mine effective data.The specific research work of this paper is illustrated as follows:1.An improved parallel coordinate visualization method based on PCA and X-Means is proposed.The method firstly uses the PCA dimensionality reduction algorithm to reduce the dimensionality of the data,then uses X-Means clustering algorithm to cluster the data,and the results of the clustering are effectively evaluated.Then,the validity of the clustering results is analyzed,and the fitting results of the clustering results are judged by the index scores of the clustering results.Finally,the graphics are processed by visual interaction technology to realize the interaction between the user and the visualization results.It is convenient for users to better understand and analyze existing data.The experimental results show that the method shortens the visualization time,effectively improves the visualization effect,and slows down the dense overlap of lines,so that users can better understand thedata and obtain the overall law of the data.2.A decision method based on the scores of clustering index is used to judge the fitting of clustering results.This method can effectively evaluate the clustering results and make a more direct judgment on the excellent characteristics of clustering results.3.An improved radial coordinate visualization method based on KNN and ReliefF is proposed,which solves the problem of poor visual clustering effect after random dimension anchor point layout.The method first normalizes the existing data,so that the features between different dimensions have certain comparability in numerical value,thereby improving the calculation accuracy.Secondly,the K-nearest neighbor classifier is used as the framework for different dimensional order.The combined radial coordinate projection results are evaluated to prove the positive relationship between the correct rate of KNN model categorization on the Radviz ring and the clustering effect.Finally,the ReliefF heuristic search method is used to improve the search for Radviz mapping and has greatly improved the projection effect.In summary,this paper studies the visualization of multidimensional data based on parallel coordinates and radial coordinates.The proposed strategies effectively improve the visualization of data,and help users deepen their understanding of data sets to a certain extent.The research results of this paper provide new theories and ideas for the future visualization of multidimensional data.