The nearest neighbor algorithm of filling missing data based on cluster analysis

摘要

Missing data universally exists in various research fields and it results in bad computational performance and effect. In order to improve the accuracy of filling in the missing data, a filling missing data algorithm of the nearest neighbor based on the cluster analysis is proposed by this paper. After clustering data analysis, the algorithm assigns weights according to the categories and improves calculation formula and filling value calculation based on the MGNN (Mahalanobis-Gray and Nearest Neighbor algorithm) algorithm. The experimental results show that the filling accuracy of the method is higher than traditional KNN algorithm and MGNN algorithm.