A tool for subjective and interactive visual data exploration

We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the data. In contrast, our generic tool computes data visualizations that are surprising given a user’s current understanding of the data. The user’s belief state is represented as a set of projection tiles. Hence, this user-awareness offers users an efficient way to interactively explore yet-unknown features of complex high dimensional datasets

An enhanced relevance criterion for more concise supervised pattern discovery

Supervised local pattern discovery aims to find subsets of a database with a high statistical unusualness in the distribution of a target attribute. Local pattern discovery is often used to generate a human-understandable representation of the most interesting dependencies in a data set. Hence, the more crisp and concise the output is, the better. Unfortunately, standard algorithm often produce very large and redundant outputs. In this paper, we introduce delta-relevance, a definition of a more strict criterion of relevance. It will allow us to significantly reduce the output space, while being able to guarantee that every local pattern has a delta-relevant representative which is almost as good in a clearly defined sense. We show empirically that delta-relevance leads to a considerable reduction of the amount of returned patterns. We also demonstrate that in a top-k setting, the removal of not delta-relevant patterns improves the quality of the result set