Density peak clustering algorithm is a new fast search algorithm for automatically finding cluster centers.Aiming at the uncertainty of its cut-off distance and the instability of the one-step allocation strategy,an improved density peak clustering approach based on African vultures optimization algorithm is proposed.The objective function of the optimization problem is established through evaluating accuracy(Acc),and the uncertain cut-off distance dc is optimized by the powerful optimization ability of the African vultures optimization algorithm,which reduces the inaccuracy of artificial values.Secondly,according to the average density of the data set,it is divided into different density areas,and different allocation strategies are used for different areas.For data points in the high-density area,the same allocation method as the original density peak clustering is used,and for data points in the low-density area,the k-nearest neighbor method is used for clustering.Finally,the algorithm is experimentally verified on synthetic and real data sets,the clustering performance of the algorithm has been greatly improved,and the division of data sets with large density differences is also more accurate.
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