Projection and ranking are frequently used analysis techniques in multi-attribute data exploration. Both families of techniques help analysts with tasks such as identifying similarities between observations and determining ordered subgroups, and have shown good performances in multi-attribute data exploration. However, they often exhibit problems such as distorted projection layouts, obscure semantic interpretations, and non-intuitive effects produced by selecting a subset of (weighted) attributes. Moreover, few studies have attempted to combine projection and ranking into the same exploration space to complement each other's strengths and weaknesses. For this reason, we propose RankAxis, a visual analytics system that systematically combines projection and ranking to facilitate the mutual interpretation of these two techniques and jointly support multi-attribute data exploration. A real-world case study, expert feedback, and a user study demonstrate the efficacy of RankAxis.
翻译：预测和排名是多属性数据勘探中经常使用的分析技术,两种技术的类别都帮助分析家查明观测之间的相似之处和确定有顺序的分组,在多属性数据勘探中表现出良好的表现,然而,它们往往出现扭曲的投影布局、模糊的语义解释和通过选择一组(加权)属性产生的非直觉效应等问题,此外,很少有研究试图将预测和排入同一勘探空间以补充对方的优势和弱点。为此,我们建议采用RankAxis,即视觉分析系统,将投影和排位系统系统地结合起来,以便利对这两种技术的相互解释,并共同支持多属性数据勘探。
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