Analyzing Company Hiring Patterns Using K-Means Clustering and Association Rule Mining: A Data-Driven Approach to Understanding Recruitment Trends in the Digital Economy

Abstract

This study explores the relationship between company characteristics and recruitment trends by analyzing a dataset obtained from Simplify.jobs, which contains detailed profiles of companies and their job postings. The research focuses on how organizational attributes such as funding stage, customer type, and company size influence recruitment strategies and job posting behaviors. Using clustering techniques, the study identifies three distinct clusters of companies based on these attributes, revealing that early-stage companies prioritize technical hires while later-stage companies offer a more diverse array of benefits. The study also employs association rule mining to uncover frequent patterns between job attributes, such as the tendency for tech companies to offer remote work options. These findings highlight how companies adapt their recruitment strategies as they grow, with early-stage companies leveraging financial incentives like stock options to attract talent, while later-stage companies emphasize employee retention through comprehensive benefits packages. The results offer valuable insights for HR professionals and recruiters, enabling them to tailor their strategies according to company profiles. This research contributes to the broader field of data-driven recruitment analysis by providing a nuanced understanding of how company characteristics shape hiring practices and job posting trends. The study also paves the way for future research into the evolution of recruitment strategies over time and the application of similar methodologies to other industries.