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The problem of reconstruction and mining object trajectories is of interest in the applications of mining transport enterprise data concerning with the route followed by its delivery vans in order to optimize time and space deliveries. The paper investigates the case of Wireless Sensor Network (WSN) technology, not primarily designed for localization, and reports a technique based on recurrent neural networks to reconstruct the trajectory shape of a moving object (a sensor on a Lego train) from the sensor accelerometer data and to recover its localization. The obtained patterns are thus mined to detecting periodic or frequent patterns, exploiting a recently proposed technique based on clustering algorithms and associative rules to assert the ability of the proposed approach to track WSN mote localizations.
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