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

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.

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 0; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916
Uncontrolled Keywords: Intelligent computing, Conventional techniques; Correlation techniques; Fuzzy C mean; Imputation techniques; Missing values; Pre-processing; Processing technique; Real-world, Data mining
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:27
Last Modified: 10 Nov 2023 03:27
URI: https://khub.utp.edu.my/scholars/id/eprint/12645

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