  Mining the distribution of features and sorting items by combined attributes
are two common tasks in exploring and understanding multi-attribute (or
multivariate) data. Up to now, few have pointed out the possibility of merging
these two tasks into a united exploration context and the potential benefits of
doing so. In this paper, we present SemanticAxis, a technique that achieves
this goal by enabling analysts to build a semantic vector in two-dimensional
space interactively. Essentially, the semantic vector is a linear combination
of the original attributes. It can be used to represent and explain abstract
concepts implied in local (outliers, clusters) or global (general pattern)
features of reduced space, as well as serving as a ranking metric for its
defined concepts. In order to validate the significance of combining the above
two tasks in multi-attribute data analysis, we design and implement a visual
analysis system, in which several interactive components cooperate with
SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We
prove the effectiveness of our system and the SemanticAxis technique via two
practical cases.
