Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

Abstract

In m-commerce services, the periodic movement trends of customers at specific periods can be adopted to allocate the resources of telecommunications systems effectively and offer personalized location-based services. This study explores the mining of periodic maximal promising movement patterns. A detailed process for mining periodic maximal promising movement patterns based on graph mapping and sampling techniques is devised to enhance mining efficiency. First, a random sample of movement paths from time intervals is taken. Second, a unique path graph structure is built to store the movement paths obtained from the sample. Third, a graph traversal algorithm is developed to identify the maximal promising movement patterns. Finally, vector operations are undertaken to examine the maximal promising movement patterns in order to derive the periodic maximal promising movement patterns. Experimental results reveal that the sampling approach with mining has excellent execution efficiency and scalability in the investigation of periodic maximal promising movement patterns.