基于类的余弦距离聚类缺失值填补方法研究  

A Study of Missing Value Imputation Methods for Class-based Cosine Distance Clustering

作　　者：夏婷婷 林康 张潇予 刘海忠 XIA Tingting;LIN Kang;ZHANG Xiaoyu;LIU Haizhong(Lanzhou Jiaotong University,Lanzhou 730070,China;Beijing Normal University,Zhuhai 519087,China;School of Social and Behavioral Sciences,City University of Hong Kong,Lanzhou 730070,China)

机构地区：[1]兰州交通大学,甘肃兰州730070 [2]北京师范大学,广东珠海519087 [3]香港城市大学社会与行为科学学院,甘肃兰州730070

出　　处：《河南科技》2024年第8期28-35,共8页Henan Science and Technology

摘　　要：【目的】为了解决欧氏距离计算相似性带来的高维度问题,提出了基于类的余弦距离聚类缺失值填补方法。【方法】首先将不完整数据集分为两个不同的组(G1和GIM);其次通过聚类中心对GIM组中的缺失数据进行预填补;再次利用余弦距离计算相关性;最后选择与G1组中距离最小的数据来填补缺失值。【结果】实验结果表明,该方法在类别和混合数据集上均优于其他插补方法。【结论】该方法显著提高了准确率、召回率、F1-score及插补效果。[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 imputationperformance.

关 键 词：不完整数据 缺失值插补 聚类 余弦距离 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP311.13[自动化与计算机技术—控制科学与工程]