The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Error-Tolerant Retrieval of Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
AGILE: A General Approach to Detect Transitions in Evolving Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
On Mining Moving Patterns for Object Tracking Sensor Networks
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
Optimizing in-network aggregate queries in wireless sensor networks for energy saving
Data & Knowledge Engineering
A framework of mining semantic regions from trajectories
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
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Prior works have shown that probabilistic suffix trees (PST) could predict accurately the moving behaviors of objects for prediction-based object tracking sensor networks. However, maintaining PSTs for objects incurs a considerable amount of storage spaces for resource-constrained sensor nodes. In this paper, we derive a distance function between two PSTs and propose an algorithm to determine the similarity between them. By the distance between PSTs, we propose a clustering algorithm to partition objects with similar moving behaviors into groups. Furthermore, for each group, one PST is selected to predict movements of objects within one group. Experimental results show that our proposed approaches not only effectively reduce the storage cost but also provide good prediction accuracy.