带分类值排列算法的改进的Parallel Sets
Improved Parallel Sets with Categories Arrangements
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摘要: 针对分类型可视化工具Parallel Sets任意排列分类值产生较多交叉的不足,提出了基于中位数的启发式分类值排列算法,自动优化分类值布局顺序,减轻视图中的可视混乱,而后应用改进的Parallel Sets分析了全球恐怖主义数据库。实验结果表明,改进的Parallel Sets可清晰展现国际恐怖主义数据库中各分类值间的关联,从而辅助用户获取不同恐怖组织的行为特征等隐性信息;基于中位数的启发式分类值排列算法简单高效,适用于数据量较大、分类值较多的数据集。Abstract: To alleviate the deficiency of excessive edge crossing brought by random layout of categories in categorical data visualization—parallel sets,we propose a heuristic layout algorithm based on median,which optimise the layout order of categories such that visual clutter is eased.And then we utilize the improved parallel sets to analyze the global terrorism database(GTD).The experimental results show that the improved parallel sets can clearly express the association among the multi-categories in GTD,thereby assist users in analyzing the implicit information of various terrorist organizations,such as the behavior characteristics.Furthermore,the median-based heuristic is simple and has high efficiency,which is suitable for large data sets with many categorical attributes.