期刊名称：Electronic Journal of Applied Statistical Analysis
电子版ISSN：2070-5948
出版年度：2018
卷号：11
期号：1
页码：369-381
语种：English
出版社：University of Salento
摘要：In this paper an adjustment on the Fuzzy K-means (FKM) clustering method was suggested to improve the process of clustering. Also, a novel technique for missing data imputation was proposed and it was implemented twice: (1) using FKM and (2) using the Enhanced Fuzzy K-means (EFKM) clustering. The suggested model for imputing missing data consists of three phases: (1) Input Vectors Partitioning, (2) Enhanced Fuzzy Clustering, and(3) Missing Data Imputation. The implementation and experiments showed a clear improvement in the imputation accuracy in favor of the EFKM according to the value of RMSE.
关键词：Missing Data Imputation; Cluster Analysis; Fuzzy K-means clustering; Data mining; Fuzzy sets; Fuzzy C-means.
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