MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis

Multiple aspect trajectory (MAT) is a relevant concept that enables mining interesting patterns moving objects for di!erent applications. This new way of looking at trajectories includes a semantic dimension, which presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. The high dimensionality and heterogeneity of these data makes clustering a very challenging task both in terms of e"ciency and quality. The present demo o!ers a tool, called MAT-CA, to support the user in the clustering task of MATs, speci#cally for identifying and visualizing the hidden patterns. The MAT-CA join into the same tool a multiple aspects trajectories clustering method and visual analysis of the results. We illustrate the use of the tool for o!ering both clustering output visualization and statistics.