The Efficiency of Hybrid K-Means and Density Peak Clustering Algorithms for Multidimensional Poverty Clustering Using Thai People Map and Analytics Platform (TPMAP) Database
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Abstract
Multidimensional poverty remains a critical challenge for sustainable development in Thailand, requiring analytical approaches that can reveal complex regional patterns of deprivation. This study evaluated the effectiveness of a hybrid clustering algorithm that integrated the K-Means and Density Peak Clustering (DPC) algorithms to categorize Thai provinces based on multidimensional poverty characteristics. The analysis used data from the Thai People Map and Analytics Platform (TPMAP), incorporating seventeen indicators across five key dimensions: living conditions, health, education, income, and access to basic services. The proposed hybrid method combined the computational efficiency of K-Means with the density-based cluster detection capability of DPC to improve clustering accuracy. The clustering results were evaluated using the Silhouette Score and visualized by scatter plots, bar charts, pie charts, and spatial maps to illustrate regional poverty patterns. The results indicated that the hybrid approach produced more coherent and well-separated clusters compared to the traditional K-Means algorithm, revealing clear inter-regional disparities in multidimensional poverty across Thai provinces. The findings suggested that poverty characteristics differ significantly by dimension and region, highlighting the importance of dimension-specific and regionally targeted policy interventions. This study demonstrated the potential of hybrid clustering algorithms as an effective data-driven tool to support evidence-based policymaking for reducing social inequality.
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