%0 Journal Article
%T An Enhanced Fuzzy K-means Clustering with Application to Missing Data Imputation
%A Hazem Migdady
%A Mohammad Mahmoud Al-Talib
%J -
%D 2018
%R DOI Code: 10.1285/i20705948v11n2p674
%X 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
%K Missing Data Imputation
%K Cluster Analysis
%K Fuzzy K-means clustering
%K Data mining
%K Fuzzy sets
%K Fuzzy C-means.
%U http://siba-ese.unisalento.it/index.php/ejasa/article/view/18599