Purpose. Cluster analysis is not only an important research field of data mining but also a significant means and method in data partitioning or packet processing. The research aims to further improve the effect of clustering algorithm and overcome the existing defects of differential evolution (DE). The research achievements are intended to be used in the cluster analysis to obtain better clustering effect. Methodology. We have made in-depth research with regards to DE algorithm and cluster analysis, discussed the effect of K-means as well as the flowchart and computing method of the fitness function. The influence of different differential operations plays on the performance have been analyzed. Findings. Firstly, we explained the fundamental ideas and methods of cluster analysis and DE algorithm. Then we illustrated how the improved DE algorithm realizes the cluster analysis. Finally, we conducted simulation experiment of cluster analysis on four artificial data through the clustering algorithm based on elite strategy DE algorithm so that we can verify the feasibility and validity of the new method. Originality. We developed an elite strategy DE algorithm and used it in K-means cluster analysis. Since DE algorithm is a method to search the optimal solution by simulating natural evolution process, its outstanding features are its implicit parallelism and ability to utilize the global information effectively, therefore, the new and improved algorithm has stronger robustness and it can avoid getting trapped in a local optimum and greatly enhances the clustering effect. The research on this aspect has not been found at present. Practical value. By applying elite strategy DE algorithm in K-means cluster analysis, we can improve the efficiency and accuracy of cluster analysis. The result of the simulation experiment showed that the new method presented in this paper has significantly improved the optimization performance, which verifies the feasibility and effectiveness of this new method.

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