基于聚类和自动编码机的缺失数据填充算法

计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (18): 13-17.

基于聚类和自动编码机的缺失数据填充算法

卜范玉1，2，陈志奎1，张清辰1

1.大连理工大学软件学院，辽宁大连 116620
2.内蒙古财经大学职业学院，呼和浩特 010010

出版日期:2015-09-15 发布日期:2015-10-13

Missing value imputation algorithm based on clustering and auto-encoder

BU Fanyu1，2, CHEN Zhikui1, ZHANG Qingchen1

1.School of Software Technology, Dalian University of Technology, Dalian, Liaoning 116620, China
2.College of Vocation, Inner Mongolia University of Finance and Economics, Huhhot 010010, China

Online:2015-09-15 Published:2015-10-13

摘要/Abstract

摘要： 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。

关键词: 不完整数据, 快速密度聚类算法（CFS）, 自动编码机, 部分距离策略

Abstract: 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.

Key words: incomplete data, Clustering by Fast Search and find of density peaks（CFS）, auto-encoder, partial distance strategy

卜范玉1，2，陈志奎1，张清辰1. 基于聚类和自动编码机的缺失数据填充算法[J]. 计算机工程与应用, 2015, 51(18): 13-17.

BU Fanyu1，2, CHEN Zhikui1, ZHANG Qingchen1. Missing value imputation algorithm based on clustering and auto-encoder[J]. Computer Engineering and Applications, 2015, 51(18): 13-17.

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