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

Abstract

C2 C3 C5 C4 Fig. 1. (A) The data loader facilitates data selection; (B) The interactive projection view shows the projection distribution and guides analysts to explore the projection layout and directional semantics; (C1 -C5) The ranking tabular view summarizes the attribute contributions to the ranking and supports the deduction of the attribute weights based on user interaction, as well as compares different ranking schemes; (D) The comparative projection view analyzes the distribution of observations generated by different ranking schemes; (E) The ranking projection axis view compares the results of projection and ranking in the same context.