Predicting future locations using clusters' centroids

As technology advances we encounter more available data on moving objects, thus increasing our ability to mine spatiotemporal 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. Categories and Subject Descriptors I.5.3 [Pattern Recognition]: Clustering- algorithms. General Terms Algorithms, Performance, Experimentation. Keywords Spatio-temporal data mining, Moving objects, Prediction, Clustering.

• Type: Conference
• Year: 2007
• Where: GIS
• Authors: Sigal Elnekave, Mark Last, Oded Maimon
• Keywords: Extracted Movement Patterns, Future Times, GIS, GIS 2007, Movement Patterns