In biomedical research a relevant issue is to identify time intervals or portions of a n-dimensional support where a particular event of interest is more likely to occur than expected. Algorithms that require to specify a-priori number/dimension/length of clusters assumed for the data suffer from a high degree of arbitrariness whenever no precise information are available, and this may strongly affect final estimation on parameters. Within this framework, spatial scan-statistics have been proposed in the literature, representing a valid non-parametric alternative.