Density clustering algorithm based on the dynamic selection of cluster center

For the existing clustering algorithms, there are some problems such as the unreasonable selection of cluster center and parameters sensitive. A density clustering algorithm based on the dynamic selection of cluster center (CCDS) is proposed. CCDS proposes a new local density metric based on the cutoff distance adaptive computing mechanism and the K-nearest neighbor idea. Then, the true cluster centers are selected according to the dynamic selection mechanism of cluster center. Finally, the remaining objects are assigned into corresponding clusters according to the minimum distance from the high-density objects. The experimental results on synthetic datasets and UCI datasets verify the effectiveness of CCDS.CCDS not only recognizes arbitrary shapes clusters, but also has high clustering accuracy on multi-density and high-dimensional large datasets.