Optimizing a radial visualization with a genetic algorithm

We consider in this paper a radial visualization called POIViz as a starting point to be improved with an optimization procedure. In our previous work, we studied POIViz and showed that it was able to represent multidimensional data in 2D within a few seconds, even for datasets with millions of records. We provided a simple heuristic to select the Points Of Interest (POIs), i.e., the 2D anchors that determine the layout of the data. In this paper, we extend POIViz to Gen-POIViz by proposing a genetic algorithm (GA) that can greatly optimize the quality of the visualization. The GA searches for a set of POIs that minimizes a cost function that is based on Kruskal's stress. Furthermore, Gen-POIViz can find relevant POIs with a small sample of the data only, and thus it can compute a projection of the complete data in a very short time. We provide comparative results with standard methods in data projection. Gen-POIViz obtains results with a quality that is between force-directed Multidimensional Scaling (MDS) and Principal Components Analysis (PCA). On larger datasets, we show the advantage of our method when it works on a data sample. It can be much faster than MDS, and it can be run with even larger datasets for which other methods fail.