Abstract: The advancement of Wireless Communication devices have created a new business model. Mobile users can request services through their mobile devices via Information Service and Application Provider (ISAP) from anywhere at any time are enhanced by mining and prediction of mobile user behaviors. But such discovery may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered simultaneously in the previous works. User relations and temporal property are used simultaneously in this work. Prediction strategy is used to predict the subsequent mobile behavior. Here CTMSP-Mine (Cluster-based Temporal Mobile Sequential Pattern - Mine) algorithm is used to mine CTMSPs. In CTMSP-Mine requires user clusters, which are constructed by Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by Location-Based Service Alignment (LBS-Alignment) to construct the user groups. The temporal property is used by time segmenting the logs using time intervals. The specific time intervals to segment the huge data logs are found using Genetic Algorithm based method called GetNTSP (Get Number of Time Segmenting Points). The user cluster information resulting from CO-Smart-CAST and the time segmentation table are provided as input to CTMSP-Mine technique, which creates CTMSPs. The prediction strategy uses the patterns to predict the mobile user behavior in the near future.