Name
Abstract | ||
|---|---|---|
One of the key tasks in mobility data analysis is the study of the individual mobility of users with reference to their personal locations, i.e. the places or areas where they stop to perform any kind of activities. Correctly discovering such personal locations is therefore a very important problem, which is yet not very well addressed in literature. In this work we propose a robust, efficient, statistically well-founded and parameter-free personal location detection process. The algorithm, called TOSCA (TwO-Steps parameter free Clustering Algorithm), combines two clustering strategies and applies statistical tests to drive the selection of the needed parameters. The proposed solution is tested against a large set of competitors and several datasets, including synthetic and real ones. The empirical results show its ability to automatically adapt to different contexts yielding good accuracy and a good efficiency. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2820783.2820818 | SIGSPATIAL/GIS |
Keywords | Field | DocType |
Personal Locations Detection, Mobility Data Mining, Clustering Algorithm | Individual mobility,Data mining,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Location detection,Computer science,Artificial intelligence,Cluster analysis,Machine learning,Statistical hypothesis testing | Conference |
Citations | PageRank | References |
9 | 0.55 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riccardo Guidotti | 1 | 112 | 24.81 |
| Roberto Trasarti | 2 | 710 | 45.82 |
| Mirco Nanni | 3 | 1412 | 84.47 |