Enhanced Visual Clustering by Reordering of Dimensions in Parallel Coordinates

The high dimensional dataset presents a serious challenge of visualization techniques such as Parallel Coordinates. The order and arrangement of dimensions in Parallel Coordinates has a major impact on the user analysis task. Therefore we need to find an expressive and effective order that helps the user to explore and analyze visual display of data mining results. This problem is the key motivation of our work. In this paper, we extended the concept of relative entropy measure like distance measure between dimensions. After the application of the proposed measure on datasets, the obtained results demonstrate that this measure is able to reorder dimensions, while the set of clusters are reorganized to help the user to detect where the clusters' behavior are similar and different. Moreover, it shows clearly the user the most important dimensions that can be used to analyze data mining results using Parallel Coordinates.