Clustering by fast search and find of density peaks (DPC) is an efficient algorithm for finding cluster centers quickly based on local-density and relative-distance. DPC uses the decision graph to find the density peaks as cluster centers. It does not need to specify the number of clusters in advance and clusters with arbitrary shapes can be obtained. However, the calculation of local-density and relative-distance depends on the similarity matrix which is based on distance metrics simply, thus, DPC is not satisfactory on complex datasets, especially when the datasets with uneven density and higher dimensions. In addition, the measurement of the local-density is not unified and different methods correspond to different datasets. Third, the measurement of d_c only considers the global distribution of datasets, ignoring the local information of the data, so the change of d_c will affect the results of clustering, especially on small scale datasets. Aiming at these shortcomings, this study proposes an optimized density peaks clustering algorithm based on dissimilarity measure (DDPC). DDPC introduces a mass-based dissimilarity measure to calculate the similarity matrix, and calculates the k-nearest neighbor information of the sample based on the new similarity matrix. Then local-density is redefined by the k-nearest neighbor information. Experimental results show that the optimized density peaks clustering algorithm based on dissimilarity measure is superior to the optimized FKNN-DPC and DPC-KNN clustering algorithms, and can be satisfied on datasets with uneven density and higher dimensions. As a result, the local-density measurement method is unified at the same time, which avoids the influence of d_c on the clustering results in the traditional DPC algorithm.