Quality Metrics Driven Approach to Visualize Multidimensional Data in Scatterplot Matrix

Abstract

Extracting meaningful information out of vast amounts of highdimensional data is very difficult. 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 the representative and generalizable quality metrics and to apply the metrics to amplify interesting patterns as well as to mute the uninteresting noise for multidimensional visualizations. In this particular poster, we investigate quality metrics driven approach to achieve the goal for scatterplot matrix (SPLOM). Our main approach is to rearrange scatterplot matrices by sorting scatterplots based upon their patterns especially locally significant ones, called scatterplot motifs. Using the approach, we expect scatterplot matrices to reveal groups of visual patterns appearing adjacent to each other, which helps analysts to gain a clear overview and to delve into specific areas of interest more easily. Our ongoing investigation aims to test and refine the feature vector for scatterplot motifs depending upon data sizes and the number of dimensions.