<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lars Kotthoff</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Barry O'Sullivan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Find Your Way Back: Mobility Profile Mining with Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Principles and Practice of Constraint Programming</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cork</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>