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.