Quality Metrics Driven Approach to Visualize Multidimensional Data in Scatterplot Matrix

Extracting meaningful information out of vast amounts of high-dimensional data is challenging. Prior research studies have been trying to solve these problems through either automatic data analysis or interactive visualization approaches. Our grand goal is to derive representative and generalizable quality metrics and to apply these to amplify interesting patterns as well as to mute the uninteresting noise for multidimensional visualizations. In this poster, we investigate a quality metrics-driven approach to achieve our goal for scatterplot matrices (SPLOMs). We rearrange SPLOMs by sorting scatterplots based on their locally significant visual motifs. Using our approach, we enable scatterplot matrices to reveal groups of visual patterns appearing adjacent to each other, helping analysts to gain a clear overview and to delve into specific areas of interest more easily.