@conference {760, title = {Find Your Way Back: Mobility Profile Mining with Constraints}, booktitle = {Principles and Practice of Constraint Programming}, year = {2015}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cork}, abstract = {Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.}, author = {Lars Kotthoff and Mirco Nanni and Riccardo Guidotti and Barry O{\textquoteright}Sullivan} }