Visual exploration of time-varying matrices

In this paper we present several extensions of our previous work on combining the multidimensional scaling technique and the reorderable matrix method to visualize time-varying matrices: (a) the Sammon mapping is employed as another dimension reduction technique that in contrast to multidimensional scaling pays more attention to small distances; (b) a novel method for the interactive colored visualization of covariances/correlations is presented; (c) the K-means clustering algorithm is used and its results are directly visualized in the mentioned dimension reduction plots; (d) a novel view, namely the visualization of the timely evolution of the cluster membership, is proposed. The latter is based on calculating a cumulated adjacency matrix that gathers the information regarding membership of objects in clusters for each point of time. The color visualization of this matrix allows the investigation of changes in cluster memberships and possible outliers, i.e. objects that change clusters frequently. Results are presented by visualizing sensitivity matrices generated during the simulation of metabolic network models.