primary topic: Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
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HTML Summary of #20682 Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
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primary topic: Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset

Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
abstract: 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, Â© 2021 IEEE.
author list: authors
status: peerReviewed
status: published
date: 2021
is part of: repository
type: AcademicArticle
type: Article
type: ConferenceItemEPrint
type: EPrint

Institute of Electrical and Electronics Engineers Inc.
type: Organization

Jafreezal Jaafar
family name: Jaafar
given name: Jafreezal
type: Person

Razulaimi Razali
family name: Razali
given name: Razulaimi
type: Person

Farahida Hanim Mausor
family name: Mausor
given name: Farahida Hanim
type: Person

Shakirah Mohd Taib
family name: Taib
given name: Shakirah Mohd
type: Person

2021 IEEE International Conference on Computing, ICOCO 2021
type: Collection