K-means clustering analysis and multiple linear regression model on household income in Malaysia

Gan Pei Yee (Universiti Tun Hussien Onn Malaysia)
Mohd Saifullah Rusiman (Universiti Tun Hussien Onn Malaysia)
Shuhaida Ismail (Universiti Tun Hussien Onn Malaysia)
Suparman Suparman (Universiti of Ahmad Dahlan)
Firdaus Mohamad Hamzah (Universiti Kebangsaan Malaysia)
Muhammad Ammar Shafi (Universiti Tun Hussien Onn Malaysia)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 12, No 2: June 2023

Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DoSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.