Applying K-Means Clustering to Group Jobs Based on Location and Experience Level: Analysis of the Job Recommendation - Kumar, Vinoth; S, Priya (International Journal for Applied Information Management, 2024)
Labor market analysis plays a crucial role in helping job seekers identify employment opportunities that align with their qualifications, location, and experience level. This study uses the K-Means clustering algorithm to group jobs based on these critical factors. By analyzing job market data, the research identifies the most sought-after skills across various industries and highlights the geographic and experience-level disparities in job availability. Key findings include the high demand for foundational skills such as customer service, sales, and production planning, as well as more specialized skills like Medical Research in certain sectors. The study provides actionable insights for job seekers and policymakers, suggesting that targeted skill development and training programs are essential for improving job match quality. However, the study also acknowledges its limitations, such as the lack of consideration for broader economic and social factors that influence labor market trends. Future research is recommended to address these gaps, using more comprehensive datasets and advanced analytical techniques.