Data Visualization Model Methods and Techniques

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Bai, Shengyuan^1 ; Zhou, Xiangyi^2 ; Lyu, You^3 ; Wang, Jiali^4 ; Pan, Chengxiang^1

Navigation College, Dalian Maritime University, Dailian, Liaoning

116026, China^1

Information College, Beijing Forestry University, Beijing

100080, China^2

Information College, Liaoning University, Shenyang,Liaoning

110136, China^3

Information College, Liaoning University, Shenyang, Liaoning

110136, China^4

关键词: Clustering data;    Dimensionality reduction;    High dimensional data;    University of California;    Visual learning;    Visual mapping;    Visualization modeling;    Visualization models;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/5/052063/pdf
DOI  :  10.1088/1755-1315/252/5/052063

来源: IOP

【 摘 要 】

In order to meet the requirements of high-dimensional data processing in the information field, this paper aims to explore methods and techniques for visualizing general data resource clustering data. Through the visual mapping of dimensionality reduction and high-dimensional data, a visual learning model for visual influencing factors is established. The visual system model approach was tested using the IRIS dataset from the University of California Irvine database (UCL) database. The results show that the model can effectively analyze the data set, visualize the characteristics of IRIS data in real time, achieve the expected results, and point the way for other data visualization models.