Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable

Missing values is one of the problems in real-world data and an unavoidable one. It should be handled carefully in a pre-processing technique before being processed in a data mining technique. This paper proposes an imputation technique of Fuzzy C Mean (FCM) with the improved version. The aim is to...

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Main Authors: Mausor, F.H., Jaafar, J., Taib, S.M.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29867 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097526325&doi=10.1109%2fICCI51257.2020.9247675&partnerID=40&md5=a41e37dcac4c9b74c59ad899dfd60d1c
http://eprints.utp.edu.my/29867/
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spelling utp-eprints.298672022-03-25T03:04:43Z Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable Mausor, F.H. Jaafar, J. Taib, S.M. Missing values is one of the problems in real-world data and an unavoidable one. It should be handled carefully in a pre-processing technique before being processed in a data mining technique. This paper proposes an imputation technique of Fuzzy C Mean (FCM) with the improved version. The aim is to reduce errors and increase the accuracy of the processing technique. In this paper, the correlation technique was applied before the process of FCM to choose the variables with a certain criterion to be processed in FCM imputation. The result shows that the proposed technique outperforms the conventional technique and useful to overcome the disadvantages of the FCM technique. © 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097526325&doi=10.1109%2fICCI51257.2020.9247675&partnerID=40&md5=a41e37dcac4c9b74c59ad899dfd60d1c Mausor, F.H. and Jaafar, J. and Taib, S.M. (2020) Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable. In: UNSPECIFIED. http://eprints.utp.edu.my/29867/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Missing values is one of the problems in real-world data and an unavoidable one. It should be handled carefully in a pre-processing technique before being processed in a data mining technique. This paper proposes an imputation technique of Fuzzy C Mean (FCM) with the improved version. The aim is to reduce errors and increase the accuracy of the processing technique. In this paper, the correlation technique was applied before the process of FCM to choose the variables with a certain criterion to be processed in FCM imputation. The result shows that the proposed technique outperforms the conventional technique and useful to overcome the disadvantages of the FCM technique. © 2020 IEEE.
format Conference or Workshop Item
author Mausor, F.H.
Jaafar, J.
Taib, S.M.
spellingShingle Mausor, F.H.
Jaafar, J.
Taib, S.M.
Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable
author_sort Mausor, F.H.
title Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable
title_short Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable
title_full Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable
title_fullStr Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable
title_full_unstemmed Missing Values Imputation Using Fuzzy C Means Based on Correlation of Variable
title_sort missing values imputation using fuzzy c means based on correlation of variable
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097526325&doi=10.1109%2fICCI51257.2020.9247675&partnerID=40&md5=a41e37dcac4c9b74c59ad899dfd60d1c
http://eprints.utp.edu.my/29867/
_version_ 1741197311861587968
score 11.62408

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