基于聚类和自动编码机的缺失数据填充算法

当前的不完整数据处理算法填充缺失值时，精度低下。针对这个问题，提出一种基于CFS聚类和改进的自动编码模型的不完整数据填充算法。利用CFS聚类算法对不完整数据集进行聚类，对降噪自动编码模型进行改进，根据聚类结果，利用改进的自动编码模型对缺失数据进行填充。为了使得CFS聚类算法能够对不完整数据集进行聚类，提出一种部分距离策略，用于度量不完整数据对象之间的距离。实验结果表明提出的算法能够有效填充缺失数据。

Missing value imputation algorithm based on clustering and auto- encoder

Existing algorithms are of low efficiency and effectiveness in imputing missing data. Aiming at this problem, the paper proposes a missing value imputation algorithm based on the CFS clustering and improved auto-encoder model. To cluster the incomplete data set, it improves the CFS clustering algorithm by introducing the partial distance strategy that is used to measure the distance between two objects with missing values. It uses the improved CFS algorithm to cluster the data set. The improved auto-encoder is used to estimate the missing values according to the clustering result. Experiments demonstrate that this proposed algorithm can impute the missing values effectively.