Abstract: For mixed data clustering, mostly current clustering algorithms have shortcomings such as low clustering efficiency, clustering parameter sensibility, clustering center number initialization and center determination difficulty. A density based cluster center self-determination mixed data clustering algorithm is proposed in this paper. Firstly, mixed data are divided into three types, including numeric dominant data, categorical dominant data and balanced data based on their data attributes analysis, and corresponding similarity metrics are designed for these three types of mixed data. Then, based on the density and distance relationship for each data object, an important conclusion is achieved that those data objects that have both higher density and larger distance than other data objects are more likely to be the cluster centers. So the linear regression model and residuals analysis are used to find those outliers that are fixed to be cluster centers automatically. The initialization value of dcis most crucial to clustering efficiency, so particle swarm optimization (PSO) algorithm is adopted to search the optimal dc by calculating the distance and density of each data object according to the automatic method for determining the cluster centers. After the cluster centers have been found, each remaining point is assigned to the same cluster as its nearest neighbor of higher density. Finally, the performance of the proposed method is testified by a series of simulations on real-world datasets in comparison with other excellent clustering algorithms.