  Accident grouping is a crucial step in identifying accident-prone locations.
Among the different accident grouping modes, clustering methods present
excellent performance for discovering different distributions of accidents in
space. This work introduces the Affinity Propagation Clustering (APC) approach
for grouping traffic accidents based on criteria of similarity and
dissimilarity between distributions of data points in space. The APC provides
more realistic representations of the distribution of events from similarity
matrices between instances. The results showed that when representative data
samples obtain, the preference parameter of similarity provides the necessary
performance to calibrate the model and generate clusters according to the
desired characteristics. In addition, the study demonstrates that the
preference parameter as a continuous parameter facilitates the calibration and
control of the model's convergence, allowing the discovery of clustering
patterns with less effort and greater control of the results
