|
|
 |
| |
|
|
|
Mining Group Moving Patterns in a Mobile Environment |
|
|
|
PP: 89-95 |
|
|
|
Author(s) |
|
|
|
Yin-Fu Huang,
Li-Kai Lin,
|
|
|
|
Abstract |
|
|
| In recent years, wireless networks and mobile applications grew rapidly. Mobile users not only request various kinds of
interesting information, but also demand on the quality of services. In this paper, we use an efficient algorithm called graph search
technique (GST) to mine the frequent moving patterns of each mobile user. For found frequent moving patterns (FMPs), we further
apply the Apriori algorithm to find the common FMPs among users. Finally, for each group of users, a characterizing method is used
to discover the relevant attribute values of the group. In the experiments, we observed that the GST algorithm has better performance
in execution time and is more stable than the AprioriAll algorithm. Furthermore, we also observed the number of found patterns under
different minimum support values and relevant percentages. |
|
|
|
|
|
|
|
