The general solution to the cluster validity problems is that one selects or defines a cluster validity index and performs a traditional clustering algorithm for all possible numbers of clusters in sequence to find the clustering structure with the best cluster validity. This is a time-consuming work. To effectively determine the optimal number of clusters in a given data set and at the same time construct the clusters with good validity, this paper presents an automatic clustering algorithm that do not require users to give each possible number of clusters. The automatic clustering algorithm treats the number of clusters as a variable and evolves it to an optimal number. In this study, in addition to the general solutions using four different traditional clustering algorithms, the proposed algorithm is employed to solve the problem of classifying the excited hearing neurons of rats. Experimental results show that the proposed clustering algorithm provides the best clustering results and the best number of classes of the excited hearing neurons is three.