A multi-center clustering algorithm based on mutual nearest neighbors for arbitrarily distributed data

Multi-center clustering algorithms have attracted the attention of researchers because they can deal with complex data sets more effectively. However, the reasonable determination of cluster centers and their number as well as the final clusters is a challenging problem. In order to solve this problem, we propose a multi-center clustering algorithm based on mutual nearest neighbors (briefly MC-MNN). Firstly, we design a center-point discovery algorithm based on mutual nearest neighbors, which can adaptively find center points without any parameters for data sets with different density distributions. Then, a sub-cluster discovery algorithm is designed based on the connection of center points. This algorithm can effectively utilize the role of multiple center points, and can effectively cluster non-convex data sets. Finally, we design a merging algorithm, which can effectively obtain final clusters based on the degree of overlapping and distance between sub-clusters. Compared with existing algorithms, the MC-MNN has four advantages: (1) It can automatically obtain center points by using the mutual nearest neighbors; (2) It runs without any parameters; (3) It can adaptively find the final number of clusters; (4) It can effectively cluster arbitrarily distributed data sets. Experiments show the effectiveness of the MC-MNN and its superiority is verified by comparing with five related algorithms.

Automated summary

This paper presents a multi-center clustering algorithm based on mutual nearest neighbors, which can effectively cluster non-convex data sets without any parameters by adaptively finding center points and utilizing the role of multiple center points, and obtaining final clusters based on the degree of overlapping and distance.