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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Zahriah Sahri
Universiti Teknikal Malaysia Melaka

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Missing values imputation in Arabic datasets using enhanced robust association rules Awsan Salem; Nurul Akmar Emran; Azah Kamilah Muda; Zahriah Sahri; Abdulrazzak Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1067-1075

Abstract

Missing value (MV) is one form of data completeness problem in massive datasets. To deal with missing values, data imputation methods were proposed with the aim to improve the completeness of the datasets concerned. Data imputation's accuracy is a common indicator of a data imputation technique's efficiency. However, the efficiency of data imputation can be affected by the nature of the language in which the dataset is written. To overcome this problem, it is necessary to normalize the data, especially in non-Latin languages such as the Arabic language. This paper proposes a method that will address the challenge inherent in Arabic datasets by extending the enhanced robust association rules (ERAR) method with Arabic detection and correction functions. Iterative and Decision Tree methods were used to evaluate the proposed method in an experiment. Experiment results show that the proposed method offers a higher data imputation accuracy than the Iterative and Decision Tree methods.
Co-Authors Abdulrazzak Ali Awsan Salem Azah Kamilah Muda Nurul Akmar Emran