PolarViz: a discriminating visualization and visual analytics tool for high-dimensional data

Visual analytics tools are of paramount importance in handling high-dimensional datasets such as those in our turbine performance assessment. Conventional tools such as RadViz have been used in 2D exploratory data analysis. However, with the increase in dataset size and dimensionality, the clumping of projected data points toward 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 a new focus + context distortion approach, termed PolarViz, to manipulate the radial distribution of data points. We derive radial equalization to automatically spread out the frequency, and radial specification to shape the distribution based on user’s requirement. Computational experiments have been conducted on two datasets including a benchmark dataset and a turbine performance simulation data. The performance of the proposed algorithm as well as other methods for solving the clumping problem in both data space and image space are illustrated and compared, and the pros and cons are analyzed. Moreover, a user study was conducted to assess the performance of the proposed method.