Combining linked matrix visualizations with multi-dimensional clustering to detect cybercrime

Our goal is to find a useful multi-dimensional clustering (MDC) of a data set from the network security domain. Algorithms from the Multi-Criteria Decision Analysis (MCDA) domain are used for the calculation of fused similarity matrices, in which the information from several dimensions are combined. However, the results of MCDA algorithms are often difficult to interpret. We present a linked, interactive, matrix-based visualization which simplifies the comparison of clusters and increases the understanding of MCDA results.