LAUSR

When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and inter-cluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.