Abstract
User behavior patterns is one of the most essential issues that need to be explored in mobile commerce. In this paper, we propose a new algorithm can efficiently discover mobile users' sequential movement patterns associated in a personal communication systems network. In the first phase of our three phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance results obtained in terms of precision and recall indicate that our method can make more accurate predictions than the other methods.
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