Abstract: The clustering algorithm based on traditional partition needs to give the number of clustering artificially, and due to the rigid partition of the algorithm, it may lead to the segmentation of large or extended clusters, leading to the wrong clustering results. Clustering by density peak is a new clustering algorithm based on density proposed in recent years. The algorithm does not need to specify the number of clusters in advance, and can detect nonspherical clusters. A fast clustering algorithm based on density peak and partition (DDBSCAN) is proposed in this paper. The algorithm first obtains the cluster center (density peak) of a group of clusters, which describes the “skeleton” of the cluster, then divides the surrounding points into the nearest core object, and finally the clusters is merged by judging the density at the dividing edge. Experiments show that the algorithm can effectively adapt to data sets of arbitrary shape and size, and converges faster than traditional clustering algorithms based on density.

Key words: clustering by density peak, cluster center, partition-based, boundary density, irregular shape