Missing values imputation in Arabic datasets using enhanced robust association rules

• Main Authors: Emran, Nurul Akmar, Draman @ Muda, Azah Kamilah, Thabet Salem, Salem Awsan, Zahriah, Sahri, Ali, Abdulrazzak
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2022
• Online Access: http://eprints.utem.edu.my/id/eprint/26431/2/28012-58448-1-PB%20PUBLISHED.PDF; http://eprints.utem.edu.my/id/eprint/26431/; https://ijeecs.iaescore.com/index.php/IJEECS/article/view/28012/16821

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.