Visual dimension analysis based on dimension subdivision (vol 24, pg 117, 2021)

作     者：Zhang, Yi Yu, Chenxi Wang, Ruoqi Liu, Xunhan 

作者机构：Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China 

出 版 物：《JOURNAL OF VISUALIZATION》 (显形杂志)

年 卷 期：2021年第24卷第5期

页      面：1113-1113页

核心收录：

学科分类：1002[医学-临床医学] 07[理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：National Natural Science Foundation of China 

主　　题：Multidimensional data Dimensional analysis Correlation analysis Multidimensional visualization 

摘      要：Visualization of multidimensional data has always been a research hotspot. Dimensional analysis is an efficient way to solve multidimensional problems. The current dimensional analysis methods mostly consider that all dimension correlations are at the same granularity, but actually the correlation between dimensions may be multi-scale. Multi-scale dimensions can also reflect the multi-scale data association mode, which is of certain value for analyzing the hidden information of multidimensional data. In this paper, we propose a method of dimension subdivision to resolve the multi-scale correlations between dimensions. To explore the multi-scale complex relationship between dimensions, we subdivide the original dimensions into finer sub-dimensions and build a graph-based data structure of the correlations to partition strongly relevant and irrelevant dimensions. We also proposed D-div, a visual dimension analysis system to support our method. In D-div, we provide visualization and interaction techniques to explore subdivided dimensions. Via case studies with two datasets, we demonstrate the effectiveness of our method of dimension subdivision.