@inbook{86126a4f92654f0a96900c643042b69a,
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. 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",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 21st International Conference on the Principles and Practice of Constraint Programming, CP 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
year = "2015",
doi = "10.1007/978-3-319-23219-5\_44",
language = "English",
isbn = "9783319232188",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "638--653",
editor = "Gilles Pesant",
booktitle = "Principles and Practice of Constraint Programming - 21st International Conference, CP 2015, Proceedings",
address = "Germany",
}