Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Myglobe: a navigation service based on cognitive maps
Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction
| Hi-index | 0.00 |
User adaptive services are important features in many applications. To provide such services, techniques with various kinds of data are being used. In this paper, we propose a method to analyze a user's past moving paths to predict the goal position and the path to the goal by observing the user's current moving path. We developed a spatiotemporal similarity measure between paths. We chose a past path that was most similar to the current path using the similarity measure. Based on the chosen path, the user's spatiotemporal position was estimated. We performed experiments to confirm this method as useful and effective