Density clustering method based on k-nearest neighbor propagation

Abstract Density clustering, an effective data analysis tool, performs well on arbitrary shapes and non-convex datasets. However, it still has some limitations in identifying the cluster structures of datasets with irregular shapes and uneven density distribution. Aiming at the above problem, this paper proposes a density clustering method based on k -nearest neighbor propagation. Firstly, based on the theory of k -nearest neighbor, the nearest hub points are defined to replace the sample points within their k -nearest neighbors, and the cluster boundary is ascertained relying on the propagation of the nearest hub points with density gradient trend so as to divide the nearest hub points into multiple groups; secondly, a group merging method of the nearest hub points based on the set similarity is given to obtain the set of nearest hub points; and finally, the remaining sample points are assigned to the set of their nearest hub points to obtain the final clusters. The correctness and effectiveness of the algorithm are verified by experiments on synthetic and UCI datasets, and the results show that the algorithm can better cope with the clustering needs of multiple complex data distributions compared with other comparative algorithms.