SubSect - An Interactive Itemset Visualization

Abstract

Itemsets and association rules are among the most simple and intuitive patterns that are used to explore transaction datasets. However, they lack meaning without both context and domain knowledge. Typically a user has to sift through hundreds of these patterns before finding an interesting one, losing sight of the forest for the trees. Furthermore, interestingness is a subjective measure that can only be approximated by objective metrics or features [3]. In previous work this problem has been tackled for instance by sorting and filtering patterns based on different metrics [3] or by trying to minimize the number of reported patterns to the most descriptive subset [1]. Another approach is to represent patterns in informative visualizations and rely on the end user to find what is interesting in their respective domain [2]. We propose a novel itemset and association rule visualization that makes it possible to inspect, assess, and compare patterns at a glance. This can not only save time and effort, but also reduce errors introduced by misconceptions. Our visualization is based on the double decker plot from Hofmann et al. [2] and exploits the monotonicity property, which states that itemsets have a lower or equal support compared to the support of their subsets.