  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.
