%X A missing value is an error that always happened, and it is unavoidable. This error should be handled correctly before data is processed into the processing model. This paper proposes a method of imputation by employing iterative Fuzzy C Means (FCM), centroid values and, fuzzy silhouette to handle missing values problem. Missing values can be treated by imputing the missing values. The advantage of FCM is it can provide a better separation of instances when an object is not well separated. It is a well-known clustering method that can provide better clustering result. The optimal clustering value can be measure by using fuzzy silhouette. In this paper, the relationship between imputation based on FCM, fuzzy silhouette and the optimal cluster is identified. Also, the factors that can give impact to accuracy of imputation is recognized, © 2021 IEEE.
%D 2021
%L scholars20682
%A Farahida Hanim Mausor
%A Jafreezal Jaafar
%A Shakirah Mohd Taib
%A Razulaimi Razali
%I Institute of Electrical and Electronics Engineers Inc.
%K Clustering algorithms; Fuzzy clustering; Clustering methods; Clustering results; Data preprocessing; Fuzzy silhouette; Imputation; Missing values; Optimal clustering; Processing model; Value problems; Iterative methods
%O Cited by: 1
%P 382 - 385
%R 10.1109/ICOCO53166.2021.9673541
%J 2021 IEEE International Conference on Computing, ICOCO 2021
%T Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset