Valuable Group Trajectory Pattern Mining Directed by Adaptable Value Measuring Model

Group trajectory pattern mining for large amounts of mobile customers is a practical task in broad applications. Usually the pattern mining result is a set of mined patterns with their support. However, most of them are not valuable for users and it is difficult for users to go through all mined patterns to find valuable ones. In this paper, instead of just mining group trajectory patterns, we investigate how to mine the top valuable patterns for users, which has not been well solved yet given the following two challenges. The first is how to estimate the value of trajectory patterns according to users’ requirements. Second, there are redundant information in the mined results because many mined patterns share common sub-patterns. To address these challenges, we define an adaptable value measuring model by leveraging multi-factors correlation in users’ requirements, which is used to estimate the value of trajectory patterns. In order to reduce the redundant sub-patterns, we propose a new group trajectory pattern mining approach directed by the adaptable value measuring model. In addition, we extend and implement the algorithm as a parallel algorithm in cloud computing platform to deal with massive data. Experiments on real massive mobile data show the effectiveness and efficiency of the proposed approach.