An efficient mobile commerce explorer for mobile user´s behavior pattern mining and prediction

Due to wide range of potential applications, research on mobile commerce has received a lot of interests from both of the industry and academia. Among them, one of the active topic areas is the mining and prediction of user´s mobile commerce behaviors such as their movements and purchase transactions. In this paper, we explore a new data mining capability for a mobile commerce environment based on location based service (LBS). A novel framework, called Mobile Commerce Explorer (MCE) used for mining and prediction of mobile users´ movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three main components like 1) Similarity Inference Model (SIM)measuring the similarities among stores and items; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm used for finding the mobile user´s Personal Mobile Commerce Patterns (PMCPs); 3) Mobile Commerce Behavior Predictor (MCBP) for prediction the future mobile user behaviors. Location-based service (LBS) is used for recommending the stores and items previously unknown to a user. The Administrator controller will monitor the whole transaction process, so there will be a secure transaction.