The border peeling (BP) clustering algorithm is a density-based clustering algorithm. It gradually peels up border points to reveal the potential cores of clusters and has been proven to be an effective clustering algorithm. However, the BP algorithm has some limitations. On the one hand, the local density of data points only considers the distance characteristics, which can lead to the unreasonable determination of border points. On the other hand, the association strategy of the BP algorithm is prone to misjudge outliers and can generate associated errors when border points are allocated. Hence, this study proposes a BP clustering algorithm based on shared nearest neighbors and optimized association strategy (SOBP). The algorithm employs a local density function based on shared nearest neighbors to better explore the similarity between data points. Meanwhile, the association strategy of the BP clustering algorithm is optimized so that in each iteration, border points are no longer associated with only one non-border point. Furthermore, a double association criterion between border points and non-border points as well as between border points peeled up is utilized. Tests on several datasets show that the proposed algorithm outperforms six other classical algorithms in terms of evaluation indexes.