@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}
}
@article {DBLP:journals/amai/FayzullinNPS00,
	title = {Foundations of distributed interaction systems},
	journal = {Ann. Math. Artif. Intell.},
	volume = {28},
	number = {1-4},
	year = {2000},
	pages = {127-168},
	author = {Marat Fayzullin and Mirco Nanni and Dino Pedreschi and V. S. Subrahmanian}
}
