文献类型：期刊文献

英文题名：Anisotropic parallel coordinates with adjustment based on distribution features

作者：Chen, Hongqian[1];Li, Hui[2];Fang, Yi[1];Chen, Yi[1]

第一作者：Chen, Hongqian

通讯作者：Li, H[1]

机构：[1]Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Management, Beijing, Peoples R China

第一机构：Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Management, Beijing, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2016

卷号：19

期号：2

起止页码：327-335

外文期刊名：JOURNAL OF VISUALIZATION

收录：;Scopus(收录号：2-s2.0-84947125074);WOS:【SCI-EXPANDED(收录号:WOS:000379431500016)】；

基金：This work is supported by the Twelfth Five-Year National Science and Technology Support Project (2012BAD29B01-2), Beijing Natural Science Foundation (4154066), the Science and Technology Plan Project of Beijing Municipal Commission of Education (PXM2014-014213-000004), Beijing Outstanding Personnel Training Program (2014000020124G029), the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (BUAA-VR-14KF-04).

语种：英文

外文关键词：Data visualization; Parallel coordinate; Anisotropic coordinate; Distribution feature

摘要：To measure the confidence degree of correlation between data dimensions in multidimensional data, we present a visualization method named anisotropic parallel coordinates. The method introduces distribution features of data into classical parallel coordinates scheme. The method first divides the data in each dimension into segments and obtains the frequency of data in each segment. The histogram is adopted to express the distribution of data in each dimension. The coordinate axis of each dimension is adjusted according to the corresponding distribution features. The principle of the adjustment is to amplify the occupation in the axis for the data segment with biggish frequency, while compacting the segment with lesser frequency. The adjustment can improve the capability of expressing the correlativity between the adjacent dimensions effectively in the final visualization result. The experimental results prove the method presented in the paper can achieve more effective expression to the correlativity between the adjacent dimension data. The improved effect can enhance the efficiency of the visual interaction and the visual analysis for the multidimensional data.