An association rule based approach to reducing visual clutter in parallel sets

Although Parallel Sets, a popular categorical data visualization technique, intuitively reveals the frequency based relationships in details, a high-dimensional categorical dataset brings a cluttered visual display that seriously obscures the relationship explorations. Association rule mining is a popular approach to discovering relationships among categorical variables. It could complement Parallel Sets to group ribbons in a meaningful way. However, it is difficult to understand a larger number of rules discovered from a high-dimensional categorical dataset. In this paper, we integrate the two approaches into a visual analytics system for exploring high-dimensional categorical data with dichotomous outcome. The system not only helps users interpret association rules intuitively, but also provides an effective dimension and category reduction approach towards a less clustered and more organized visualization. The effectiveness and efficiency of our approach are illustrated by a set of user studies and experiments with benchmark datasets.