MDS-Tree and MDS-Matrix for High Dimensional Data Visualization

In this work, we propose MDS-Tree and MDS-Matrix as novel high dimensional data visualization methods to gain insight in both the data aspect and dimension aspect of the data. Dimension metrics of the high dimensional dataset are first computed to create a hierarchy. In an MDS-Tree, each node is an MDS projection of the original data items on a specific subset of dimensions associated with the dimension hierarchy. While the MDS-Tree visualizes the subspace structure of the data under exploration, MDSMatrix provides cross comparison between different combination of subspaces. In the MDS-Matrix construction, each pair of the subspaces are plotted as an MDS plot in the MDS-Matrix. In both MDS-Tree and MDS-Matrix, a full spectrum of user interaction has been designed, including drilling down to explore different levels of the data, merging or splitting the nodes to adjust the dimension hierarchy, applying brushing to select data clusters. Our proposed MDS-Tree and MDS-Matrix reveal the data from different aspects. They enable a simultaneous exploration on the data correlation and dimension correlation for data with high dimensions.