In the radio frequency identification（RFID）communication system， when multiple tags conflict， the conflicting signals can be separated and then decoded to improve the communication efficiency， and the signal separation usually depends on clustering. However， the traditional methods cannot consider the time complexity and clustering accuracy. In this paper， a clustering method of maximum posterior probability estimation is proposed. The peak value is quickly found out by the Monte Carlo method that is used as the clustering center to complete signal separation. In the experiment， the simulation data and the measured data of software radio are used to test the proposed algorithm， and the results show that the proposed algorithm has a higher clustering accuracy and a lower time complexity under high signal noise ratio（SNR）. It is embedded in dynamic frame ALOHA system， and the throughput can reach 0.55， higher than that of the pure dynamic frame ALOHA.