Non-parameter clustering algorithm based on chain propagation and natural neighbor

Clustering analysis is a powerful tool for discovering potential knowledge in datasets. However, numerous existing clustering algorithms suffer from heavy reliance on parameter settings and cannot cluster complex manifold data very well. Moreover, although non-parameter clustering algorithms aim to lower the usage threshold, their actual performance in clustering is often unsatisfactory. Addressing how to achieve similar clustering effects for numerous data points with only a few core data points during clustering is a valuable consideration. To alleviate these challenges, a non-parameter clustering algorithm is proposed and named Non-parameter Clustering Algorithm based on Chain Propagation and Natural Neighbor(NPCCPN) in this paper, by jointly using chain propagation and natural neighbor. Specifically, NPCCPN identifies core points through chain propagation and clusters them using the saturated neighborhood graph. This makes the core data extraction and clustering process efficient and non-parameter. Finally, the performance of the method is validated on 15 complex synthetic datasets and 10 real datasets from public UCI database. The experimental results show that the effectiveness and superiority of the proposed algorithm. Purity scores first. ACC scores second without parameters, but the score first algorithm needs to set the parameters.