基于类的余弦距离聚类缺失值填补方法研究

[Purposes] In order to solve the high dimension problem caused by the similarity of Euclidean distance calculation, a class-based cosine distance clustering missing value imputation approach is proposed. [Methods] Firstly, the incomplete data set is divided into two different groups (G1 and GIM); secondly, the missing data in the GIM group is pre-filled by the clustering center; the cosine distance is used again to calculate the correlation ; finally, the data with the smallest distance from the G1 group is selected to fill the missing values. [Findings] The experimental results show that the proposed method outperforms other imputation methods for both categorical and mixed datasets. [Conclusions] The CBCIM-COS method significantly improves accuracy, recall and F1-score and imputation performance.