An Approach to Improving Nearest Neighbor Search

In this paper, we present our research on data analysis and nearest neighbor search problems. A nearest neighbor search problem is normally described as finding data point or data points from a data set that are closest to a given query point. It is used in many research and industrial fields. In our paper, we propose an approach that explores the meaning of K nearest neighbors from a new perspective. We study the distances between data points and a given query point Q, and design a process to update the distance values based on the nearest neighbor search in the past. Our approach can be used to efficiently and effectively select data points which are closest to Q, and it can be applied in various data mining fields. We conduct experiments to test our approach on both synthetic and real data sets.