关键词: 高维相关性数据, 缺失数据, 分块填补算法

Abstract: This paper studies the method of filling the high dimensional correlation missing data, and proposes a new imputation algorithm based on data block. The key idea of the algorithm is to consider the correlation between variables when filling missing data, and only use the data correlated with the missing data to fill, thereby reducing imputation effects of the missing data caused by the irrelevant data, and improving the accuracy of data imputation. At the same time, the proposed imputation algorithm can be implemented in a parallel way, so that it performs efficiently to fill the high dimensional missing data. In order to divide the missing data with unknown information about blocks into several blocks, this paper proposes a block algorithm based on k-means clustering. Simulation     research and application show that the proposed imputation algorithm is more effective and accurate to handle the missing for the correlation high dimensional data with considering variables?? block relationship than others with not.

Key words: high dimensional correlation data, missing data, block imputation algorithm