Application of K-Means Clustering for Job Applicant Analysis in Construction Firms Using R

This study applies K-Means clustering to segment job applicant test data from a construction consulting firm to support data-driven screening decisions. From 161 applicants, 30 candidates who met the document-screening requirements were invited for in-person testing and included in the analysis. Three assessment variables were used: AutoCAD drafting skills, planning/supervision report-writing skills, and adaptability. Using R, K-Means clustering was performed to partition candidates into three groups based on multivariate similarity patterns, and the resulting group structure was visualized using 2D and 3D scatter plots. The clustering output revealed distinct competency profiles: one group characterized by generally lower scores across the three variables, a second group with moderate and mixed scores, and a third group with consistently higher scores. Internal validity indices suggested modest separation (mean silhouette = 0.16; Davies-Bouldin Index = 2.05), consistent with exploratory clustering on a small pre-screened sample. These patterns provide a structured interpretation of applicant diversity and can inform practical recruitment actions such as prioritizing candidates for interviews, identifying borderline profiles for additional evaluation, and designing targeted upskilling recommendations for specific competency gaps. Overall, this study illustrates how unsupervised clustering of routine recruitment test results may support more structured interpretation of applicant competency profiles in early-stage construction-sector recruitment, provided that the results are used cautiously alongside professional judgment and further validation.