Histogram equalization and specification for high-dimensional data visualization using RadViz

In our turbine performance assessment, we need to provide an effective visual analytics tool in handling high-dimensional datasets. We have employed RadViz in 2D exploratory data analysis. However, with the increase of dataset size and dimensionality, the clumping of projected data points towards the origin in RadViz causes low space utilization, which largely degenerates the visibility of the feature characteristics. In this study, to better evaluate the hidden patterns in the center region, we propose histogram-based techniques to manipulate the radial distribution of data points in RadViz. We present RadViz in the polar coordinate system for convenient radial operations. Based on this, we define the radial equalization method to automatically spread out the frequency and the radial specification method to shape the distribution based on the user's requirement. Furthermore, we utilize the information in high-dimensional space as histogram and reference point to design and control the radial distribution of RadViz. Computational experiments have been conducted on turbine performance simulation data. Our proposed techniques are shown advantageous in query result display and outlier detection with a set of high-dimensional datasets.