Purpose. The traditional K-means algorithm requires the K value, and it is sensitive to the initial clustering center. Different initial clustering centers often correspond to the different clustering results, and the K value is always required. Aiming at these shortcomings, the article proposes a method for getting the clustering center based on the density and max-min distance means. The selection of the clustering center and classification can be carried out simultaneously. Methodology. According to the densities of objects, the noise was eliminated and the densest object was selected as the first clustering center. The max-min distance method was used to search the other best cluster centers, at the same time, the cluster, which the object belongs to, was decided. Findings. Clustering results are related to the selection of parameters θ. If the sample distribution is unknown, only test method can be used through multiple test optimization. With prior knowledge for the selection of θ, it can be converged quickly. Therefore, θ should be optimized. Originality. This article proposes the new method based on the density to get the first initial clustering center, and then the new method based on the maximum and minimum value. The improved algorithm obtained through experimental analysis insures higher and stable accuracy. Practical value. The experiments showed that the algorithm allows for automatic obtaining of the k clustering centers and have a higher clustering accuracy in unknown datasets processing.

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