A Target Clustering Method Based on Principal Component Analysis and Density Peaks Clustering

To enhance the accuracy and computational efficiency of target clustering, a WPCA-KNDPC method based on principal component analysis and density peaks clustering is proposed. Weighted principal component analysis is employed to extract features from target attribute data, reducing the computational complexity in target clustering. On the basis of the standard density peaks clustering algorithm, the density estimation model is modified by incorporating k-nearest neighbors and natural nearest neighbors, the KNDPC algorithm for target clustering is designed, and the accuracy of target clustering is improved. The simulation results demonstrate that the proposed method obviously outperforms other comparative methods in target clustering performance, exhibiting good feasibility and effectiveness.