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The clustering by fast search and find of density peaks (DPC) is a density- based clustering algorithm proposed in recent years, which has the advantages of simple principle, no iteration and clustering of arbitrary shape. However, the algorithm still has some defects: clustering around clustering centers makes the clustering results significantly affected by central points, and the number of clustering centers needs to be manually specified; the cutoff distance considers the distribution density of the data but ignores the internal features; if there is a sample allocation error in the clustering process, the subsequent sample clustering may amplify the error. To solve the above problems, this paper proposes a density-peak clustering algorithm on decentralized and weighted clusters merging (DCM-DPC). This algorithm introduces the weight to redefine the local density, dividing core sample groups located in different local high density regions to replace cluster centers as the cluster basis. Finally, the remaining samples are assigned to the highest coupled core sample groups or labeled as discrete points by their near neighbor samples. Experiments on artificial and UCI datasets show that the clustering performance of the proposed algorithm outperforms the contrast algorithms, and the boundary samples partition of the entangled clusters is more accurate. [ABSTRACT FROM AUTHOR]