A study on parameter estimation for a mining flock algorithm

Due to the diffusion of location-aware devices and location- based services, it is now possible to analyse the digital trajectories of human mobility through the use of mining algorithms. However, in most cases, these algorithms come with little support for the analyst to actu- ally use them in real world applications. In particular, means for under- standing how to choose the proper parameters are missing. This work improves the state-of-the-art of mobility data analysis by providing an experimental study on the use of data-driven parameter estimation mea- sures for mining flock patterns. Experiments were conducted on two real world datasets, one dealing with pedestrian movements in a recreational park and the other with car movements in a coastal area. The study has shown promising results for estimating suitable values for parameters for flock patterns envisaging a formal framework for parameter evaluation in the near future, since the advent of more complex pattern algorithms will require the use of a larger number of parameters.