A density clustering method based on eXtended Classifier Systems (XCS) was proposed, which could be used to cluster the two-dimensional data sets with arbitrary shapes and noises. The proposed method was called Density XCS Clustering (DXCSc), which mainly included the following three processes:1) Based on the learning classification system, regular population of input data was generated and compressed. 2) The generated rules were regarded as two-dimensional data points, and then the two-dimensional data points were clustered based on idea of density clustering. 3) The regular population after density clustering was properly aggregated to generate the final regular population. In the first process, the learning classifier system framework was used to generate and compact the regular population. In the second process, the rule cluster centers were characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. In the third process, the relevant clusters were properly merged using the graph segmentation method. In the experiments, the proposed DXCSc was compared with K-means, Affinity Propagation (AP) and Voting-XCSc on a number of challenging data sets. The experimental results show that the proposed approach outperforms K-means and Voting-XCSc in precision.