A novel method of mining the probatilistic RFID events semantic regions based on Markov trajectories

With a massive using of mobile devices with location sensing and positioning functions, such as GPS and RFID, people now are able to acquire present locations and collect their movement. As the availability of trajectory data prospers, mining activities hidden in raw trajectories becomes a hot research problem. In this paper, we consider all spatial, temporal and the probability relationships among data points of trajectories to extract the stop location of ROI (region of interest) that refer to regions in where users are likely to have some kinds of activities. In order to extract such locations, we propose a probabilistic Sequential RFID events Density Clustering Algorithm (PSRDC) to mining clusters. Based on PSRDC clustering approach, we develop a ROI of RFID Probability events streams mining Algorithm (ROI-RFID) Experimental results demonstrate that our techniques are available and feasible.