Decreasing Occlusion and Increasing Explanation in Interactive Visual Knowledge Discovery

Lack of explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This thesis proposes a hybrid method that combines visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D in a set of Shifted Paired Coordinates (SPC). SPC for n-D data consists of n/2 pairs of Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies pattern discovery in n-D data providing explainable rules in a visual form with significantly decrease of the cognitive load for analysis of n-D data. The computational experiments on real data has shown its efficiency on both training and validation data