Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23219-5_44 | CP |
Field | DocType | Volume |
Drawback,Cluster (physics),Data mining,Mathematical optimization,Computer science,Constraint programming,Constraint satisfaction problem,Cluster analysis,TRIPS architecture,Trajectory | Conference | 9255 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lars Kotthoff | 1 | 229 | 24.12 |
| Mirco Nanni | 2 | 1412 | 84.47 |
| Riccardo Guidotti | 3 | 112 | 24.81 |
| barry osullivan | 4 | 74 | 17.27 |