Label Placement for Outliers in Scatterplots

Abstract

In many application scenarios, outliers can be associated with specific importance for various reasons. In such cases, labeling outliers is important to connect them to the actual semantics of the respective entity. In this paper, we present a cost-based greedy approach that places labels with outliers within scatterplots. The approach uses a search strategy to find the position that represents the least cost to place labels. Our approach can also produce different labeling outcomes by adjusting the weights of the criteria of the cost function. We demonstrate our approach with scatterplots produced from object-oriented software metrics, where outliers often relate to bad smells in the software.