Abstract: Traditional clustering methods often cannot avoid the selection of neighborhood parameters and the number of clusters. The optimal selection of these parameters in different shapes of data is hard to choose, and this choice is depending on prior knowledge. Aiming at the above parameter selection problem, this paper proposes a natural neighbors based border peeling clustering algorithm (NaN-BP), which can obtain satisfactory clustering results without setting the neighborhood parameters and the number of clusters. The core idea of the algorithm is to adaptively iterate to a logarithmic stable state and obtain neighborhood information according to the distribution characteristics of the data set, then mark and strip the boundary points according to the neighborhood information, and finally gather the core points as the center of the data cluster. Extensive comparative experiments is conducted on data sets of different scales and distributions, and satisfactory experimental results verify the adaptability and effectiveness of the algorithm.

Key words: clustering analysis, self-adaptive, natural neighbor, logarithmic steady state, core point