Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Global distance-based segmentation of trajectories
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
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As technology advances we encounter more available data on moving objects, thus increasing our ability to mine spatio-temporal data. We can use this data for learning moving objects behavior and for predicting their locations at future times according to the extracted movement patterns. In this paper we cluster trajectories of a mobile object and utilize the accepted cluster centroids as the object's movement patterns. We use the obtained movement patterns for predicting the object location at specific future times. We evaluate our prediction results using precision and recall measures. We also remove exceptional data points from the moving patterns by optimizing the value of an exceptions threshold.