Natural neighbor: A self-adaptive neighborhood method without parameter K

K-nearest neighbor (KNN) and reverse k-nearest neighbor (RkNN) are two bases of many well-established and high-performance pattern-recognition techniques, but both of them are vulnerable to their parameter choice. Essentially, the challenge is to detect the neighborhood of various data sets, while utterly ignorant of the data characteristic. In this paper, a novel concept in terms of nearest neighbor is proposed and named natural neighbor (NaN). In contrast to KNN and RkNN, it is a scale-free neighbor, and it can reflect a better data characteristics. This article discusses the theoretical model and applications of natural neighbor in a different field, and we demonstrate the improvement of the proposed neighborhood on both synthetic and real-world data sets.