Statistics-Driven Localization of Dissimilarities in Data

The identification of dissimilar regions in spatial and temporal data is a fundamental part of data exploration. This process takes place in applications, such as biomedical image processing as well as climatic data analysis. We propose a general solution for this task by employing well-founded statistical tools. From a large set of candidate regions, we derive an empirical distribution of the data and perform statistical hypothesis testing to obtain p-values as measures of dissimilarity. Having p-values, we quantify differences and rank regions on a global scale according to their dissimilarity to user-specified exemplar regions. We demonstrate our approach and its generality with two application scenarios, namely interactive exploration of climatic data and segmentation editing in the medical domain. In both cases our data exploration protocol unifies the interactive data analysis, guiding the user towards regions with the most relevant dissimilarity characteristics. The dissimilarity analysis results are conveyed with a radial tree, which prevents the user from searching exhaustively through all the data.