As an unsupervised learning technology, clustering has been widely used in practice. However, some mainstream algorithms still have problems such as incomplete noise removal and inaccurate clustering results for the datasets with noise. In this paper, an automatic clustering algorithm based on density difference (CDD) is proposed to realize automatic classification of the datasets containing the noise. The algorithm is based on the density difference between noise data and useful data to achieve removing noise and data classification. Moreover, the useful data are classified into different classes through the neighborhood construction procedure. Experimental results demonstrate that the CDD algorithm has high performance.