Dynamic iterative imputation algorithm based on rough fuzzy C-Means clustering

Abstract

Abstract Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm. The algorithm incorporates dynamic approximations to partition and iterate the cluster assignment of missing sample points, utilizing information from complete sample points within the same cluster for iterative imputation. The imputation results are evaluated by using MAE and RMSE metrics, and the optimal number of iterations and imputation values are determined by minimizing these metrics. To verify the effectiveness of the proposed algorithm, 6 UCI public datasets are selected for imputation tests, and the change curves of metric value with iterations are generated under different missing proportions in each dataset. The optimal imputation result is obtained based on the lowest point of the curve. Subsequently, the performance of the proposed algorithm is compared with three cluster-based and five machine learning-based imputation algorithms. The results demonstrate the significant superiority of the proposed algorithm over the other methods, achieving a more approximate imputation of missing values in the dataset.