Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset

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 m...

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Main Authors: Mausor, F.H., Jaafar, J., Taib, S.M., Razali, R.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29131 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125305962&doi=10.1109%2fICOCO53166.2021.9673541&partnerID=40&md5=84bd38086f154b2dbc30c8932c25a78c
http://eprints.utp.edu.my/29131/
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spelling utp-eprints.291312022-03-25T00:57:57Z Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset Mausor, F.H. Jaafar, J. Taib, S.M. Razali, R. 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. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125305962&doi=10.1109%2fICOCO53166.2021.9673541&partnerID=40&md5=84bd38086f154b2dbc30c8932c25a78c Mausor, F.H. and Jaafar, J. and Taib, S.M. and Razali, R. (2021) Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset. In: UNSPECIFIED. http://eprints.utp.edu.my/29131/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Conference or Workshop Item
author Mausor, F.H.
Jaafar, J.
Taib, S.M.
Razali, R.
spellingShingle Mausor, F.H.
Jaafar, J.
Taib, S.M.
Razali, R.
Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
author_sort Mausor, F.H.
title Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
title_short Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
title_full Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
title_fullStr Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
title_full_unstemmed Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset
title_sort iterative fuzzy c means, fuzzy silhouette, and imputation for missing values in a dataset
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125305962&doi=10.1109%2fICOCO53166.2021.9673541&partnerID=40&md5=84bd38086f154b2dbc30c8932c25a78c
http://eprints.utp.edu.my/29131/
_version_ 1741197193544466432
score 11.62408

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