@inproceedings{kotthoff_find_2015,
  address = {Cork, Ireland},
  title = {Find {Your} {Way} {Back}: {Mobility} {Profile} {Mining} with {Constraints}},
  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.{\textbackslash} 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.},
  booktitle = {{CP}},
  author = {Kotthoff, Lars and Nanni, Mirco and Guidotti, Riccardo and O'Sullivan, Barry},
  month = aug,
  year = {2015},
  pages = {638--653},
  month_numeric = {8}
}