Abstract Multiple aspects trajectory (MAT) is a relevant concept that enables mining useful patterns and behaviors of moving objects for different applications. As a new way of looking at trajectories, MAT includes a semantic dimension, and thus presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. Considering the possibilities of this new algorithmic paradigm, we decided to test it on political data. More specifically, we look at legislative voting behavior to understand political alignment, coalition dynamics, and governance patterns. Traditional data mining approaches do not capture the temporal motifs of parliamentary voting patterns. We address this gap by employing the MAT-Tree algorithm, a hierarchical clustering method for multiple aspects trajectories, to analyze twenty years of voting data of the Brazilian Chamber of Deputies. We aim to reveal hidden patterns, such as voting similarities and alignments, by analyzing the data from the perspective of multiple aspects, thereby enabling a multidimensional analysis of voting patterns. The experimental results demonstrate that MAT-Tree identifies cohesive voting blocks, shifts in legislative support, and outlier behaviors across different political periods. Furthermore, the analysis reveals critical patterns, including increased polarization in post-impeachment periods and evolving dynamics between government and opposition. Thus, these findings highlight the potential of MAT clustering with MAT-Tree as a robust tool for political analysis, providing a scalable framework for exploring multidimensional datasets that go beyond mobility data.
Santos et al. (Fri,) studied this question.