Name
Abstract | ||
|---|---|---|
A novel methodology for efficiently sampling Trajectory Databases (TD) for mobility data mining purposes is presented. In
particular, a three-step unsupervised trajectory sampling methodology is proposed, that initially adopts a symbolic vector
representation of a trajectory which, using a similarity-based voting technique, is transformed to a continuous function that
describes the representativeness of the trajectory in the TD. This vector representation is then relaxed by a merging algorithm,
which identifies the maximal representative portions of each trajectory, at the same time preserving the space-time mobility
pattern of the trajectory. Finally, a novel sampling algorithm operating on the previous representation is proposed, allowing
us to select a subset of a TD in an unsupervised way encapsulating the behavior (in terms of mobility patterns) of the original
TD. An experimental evaluation over synthetic and real TD demonstrates the efficiency and effectiveness of our approach.
|
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15939-8_2 | Principles of Data Mining and Knowledge Discovery |
Keywords | Field | DocType |
mobility pattern,space-time mobility pattern,unsupervised trajectory sampling,symbolic vector representation,real td,original td,algorithm operating,three-step unsupervised trajectory,vector representation,mobility data mining purpose,previous representation,sampling,data mining,space time | Continuous function,Data mining,Voting,Representativeness heuristic,Sampling (statistics),Merge (version control),Trajectory,Mathematics | Conference |
Volume | ISSN | ISBN |
6323 | 0302-9743 | 3-642-15938-9 |
Citations | PageRank | References |
11 | 0.72 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikos Pelekis | 1 | 881 | 59.28 |
| Ioannis Kopanakis | 2 | 264 | 16.68 |
| Costas Panagiotakis | 3 | 175 | 22.91 |
| Yannis Theodoridis | 4 | 3155 | 266.14 |