With terrorism aggravating, anti-terrorism has been a main task for national military security deparments around the world. The current study ,utilized categorical data visualization, Parallel Sets, to analyze the relations among the multi-categories in Clobal Terrorism Database, aimed to uncover the implicit information within the data set. To alleviate the deficiency of excessive edge crossing brought by random lavout of categorical values, the research proposed a heuristic layout algorithm based on average heuristic with cardinality reduction, which optimized the layout order of categories and the visual clutter is eased so that the cardinality reduction strategies can reduce the numbemd of categories involved in computation. The experimental results demonstrate that the improved parallel sets can clearly express the association among the multi-categories in Global Terrorism Database, thereby assist users in analyzing the information of various terrorist orgardzations, such as the behavior characteristics. Furthermore, the average-based heuristic with cardinahty reduction is simple and highly efficient, which is suitable for large data sets with many categorical attributes.%随着恐怖主义愈演愈烈,"反恐"成为当今世界各国军事安全部门的中心任务.使用分类型可视化工具Parallel Sets分析国际恐怖主义数据库中多属性分类值间的关系,揭示数据库中的隐性信息,并针对Parallel Sets任意排列分类值产生较多交叉的不足,提出带降势的启发式分类值布局算法,自动优化分类值布局顺序,减轻视图中的可视混乱,降势策略可以减少参与计算的分类值数目.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,从而辅助用户分析不同恐怖组织的行为特征等信息;带降势的启发式分类值布局算法简单高效,适用于数据量较大、分类值较多的数据集.
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