SemanticAxis: Exploring Multi-attribute Data by Semantics Construction and Ranking Analysis

TL;DR

SemanticAxis introduces an interactive method for constructing semantic vectors in 2D space to unify feature distribution analysis and item ranking in multi-attribute data exploration.

Contribution

It presents a novel technique and visual system that merge data distribution and ranking tasks, enhancing multi-attribute data analysis capabilities.

Findings

Effective in representing and explaining abstract concepts in data

Improves data exploration by unifying analysis tasks

Validated through practical case studies

Abstract

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.