The Quasi-circular Mapping Visualization Based on Extending and Reordering Dimensions for Visual Clustering Analysis

Radial coordinate visualization (RadViz) and Star Coordinates (SC) can effectively map high dimensional data to low dimensional space, owing to which can place an arbitrary number of Dimension Anchors (DAs). Nevertheless, the problem owner is faced with ordering DAs, which is a NP-complete problem and visual results of crowding which hamper clustering analysis. We introduce a new radial layout visualization, called the Quasi-circular mapping visualization (QCMV), to address those problems in this paper. Firstly, QCMV extend the original dimension of datasets by the probability distribution histogram of the dimension and affinity propagation (AP) algorithm. In additional, distributing them on the unit circle by their correlation according to the correlation of the extended dimensions. Then, mapping the dimensions extended and reordered data to integrate a polygon in the Quasi-circular space and visualizing them by the geometric center and area of the polygon in the three dimension. Finally strengthening their visual clustering effect with t-SNE. We also compare the visual clustering results of RadViz, SC and QCMV with two indexes, correct rate and Dunn index on visually analyzing the three datasets. It shows better effect of visual clustering with QCMV.