Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy

Recently a density peaks based clustering algorithm (dubbed as DPC) was proposed to group data by setting up a decision graph and finding out cluster centers from the graph fast. It is simple but efficient since it is noniterative and needs few parameters. However, the improper selection of its parameter cutoff distance dc will lead to the wrong selection of initial cluster centers, but the DPC cannot correct it in the subsequent assignment process. Furthermore, in some cases, even the proper value of dc was set, initial cluster centers are still difficult to be selected from the decision graph. To overcome these defects, an adaptive clustering algorithm (named as ADPC-KNN) is proposed in this paper. We introduce the idea of K-nearest neighbors to compute the global parameter dc and the local density ρi of each point, apply a new approach to select initial cluster centers automatically, and finally aggregate clusters if they are density reachable. The ADPC-KNN requires only one parameter and the clustering is automatic. Experiments on synthetic and real-world data show that the proposed clustering algorithm can often outperform DBSCAN, DPC, K-Means++, Expectation Maximization (EM) and single-link.