A technique for extracting behavioral sequence patterns from GPS recorded data

The mobile wireless market has been attracting many customers. Technically, the paradigm of anytime-anywhere connectivity raises previously unthinkable challenges, including the management of million of mobile customers, their profiles, the profiles-based selective information dissemination, and server-side computing infrastructure design issues to support such a large pool of users automatically and intelligently. In this paper, we propose a data mining technique for discovering frequent behavioral patterns from a collection of trajectories gathered by Global Positioning System. Although the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and temporal constraints on spatiotemporal sequences makes the computation feasible. Specifically, the mined patterns are incorporated with synthetic constraints, namely spatiotemporal sequence length restriction, minimum and maximum timing gap between events, time window of occurrence of the whole pattern, inclusion or exclusion event constraints, and frequent movement patterns predictive of one ore more classes. The algorithm for mining all frequent constrained patterns is named cAllMOP. Moreover, to control the density of pattern regions a clustering algorithm is exploited. The proposed method is efficient and scalable. Its efficiency is better than that of the previous algorithms AllMOP and GSP with respect to the compactness of discovered knowledge, execution time, and memory requirement.