In order to solve the problem that Clustering by Fast Search and Find of Density Peaks (CFSFDP) needs to manually select the center on the decision graph, an Integrated Algorithm Based on Density Peaks and Density-based Clustering (IABDPDC) was proposed. Firstly, learning from the principle of CFSFDP, the data with the largest local density was selected as the first center. Then, from the first center, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm improved by Warshall algorithm was used to cluster to obtain the first category. Finally, from the data that has not been clustered, the maximum local density data was found out as the center of the next category and was clustered again by the above algorithm, until all the data was clustered or some data was considered as noise. The proposed algorithm not only solves the problem of manual center selection in CFSFDP, but also optimizes the DBSCAN algorithm, in which, every iteration starts from the current best point (the point with the largest local density). By comparing with the classical algorithms (such as CFSFDP, DBSCAN, fuzzy C-means (FCM) and K-means) on visual datasets and non-visualized datasets, the experimental results show that the proposed algorithm has better clustering effect with higher accuracy.