RankAxis: Towards a Systematic Combination of Projection and Ranking in Multi-Attribute Data Exploration

Qiangqiang Liu, Yukun Ren, Zhihua Zhu, Dai Li, Xiaojuan Ma, Quan Li

Abstract: 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RankAxis</i> , 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RankAxis</i> .