Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method(NNM) and Expectation-Maximization (EM) Algorithm

• Main Authors: Muhammad Izzuddin Rumaling, Chee, Fuei Pien, Jedol Dayou, Chang, Jackson Hian Wui, Steven Soon Kai Kong, Justin Sentian
• Format: Article
• Language: English; English
• Published: 2020
• Subjects: Q Science (General)
• Online Access: https://eprints.ums.edu.my/id/eprint/26219/1/Missing%20Value%20Imputation%20for%20PM10%20Concentration%20in%20Sabah%20using%20Nearest%20Neighbour%20Method%28NNM%29%20and%20Expectation-Maximization%20%28EM%29%20Algorithm.pdf; https://eprints.ums.edu.my/id/eprint/26219/2/Missing%20Value%20Imputation%20for%20PM10%20Concentration%20in%20Sabah%20using%20Nearest%20Neighbour%20Method%28NNM%29%20and%20Expectation-Maximization%20%28EM%29%20Algorithm1.pdf; https://eprints.ums.edu.my/id/eprint/26219/; https://doi.org/10.5572/ajae.2020.14.1.062

Missing data in large data analysis has affected further analysis conducted on dataset. To fill in missing data, Nearest Neighbour Method(NNM) and Expectation Maximization(EM) algorithm are the two most widely used methods. Thus, this research aims to compare both methods by imputing missing data of...