This paper aims to develop a clustering method that need not predefine the number of clusters or incur a high computing cost. For this purpose, Dirichlet Process Mixture Model (DPMM) which based on nonparametric Bayesian method was introduced. Three datasets, from simple to complex, were selected for experiment. The results of the first two datasets showed that the DPMM is highly flexible and reliable, because it did not need to know the number of clusters in advance and had robustness for different rational parameters. However, the DPMM failed...
