基于聚类算法的人才招聘方法研究
Research on Talent Recruitment Methods Based on Clustering Algorithms

随着信息技术的迅猛发展，尤其是大数据和人工智能技术的广泛应用，传统的人力资源管理方式正面临着前所未有的挑战和变革。本文旨在探讨基于聚类算法的人才招聘方法，通过引入先进的数据挖掘技术，优化传统的招聘流程，提升招聘效率和效果。研究采用K均值聚类和层次聚类两种方法，对模拟生成的候选人数据进行聚类分析，并通过肘部法确定最佳聚类数，使用轮廓系数评估聚类效果。结果表明，聚类算法能够有效地对候选人进行分类，帮助企业更精准地找到与岗位高度匹配的候选人，提高招聘质量和效率。本文的研究不仅为企业提供了实用的招聘方法，也为相关领域提供了有价值的参考。
With the rapid advancement of information technology, especially the widespread application of big data and artificial intelligence technologies, traditional human resource management methods are facing unprecedented challenges and transformations. This paper aims to explore talent recruitment methods based on clustering algorithms by introducing advanced data mining techniques to optimize traditional recruitment processes and enhance recruitment efficiency and effectiveness. The study employs K-means clustering and hierarchical clustering methods to analyze simulated candidate data. The optimal number of clusters is determined using the elbow method, and the effectiveness of clustering is evaluated using silhouette coefficients. The results indicate that clustering algorithms can effectively classify candidates, helping enterprises more accurately find candidates who are highly matched to job positions, thereby improving recruitment quality and efficiency. This research not only provides practical recruitment methods for enterprises but also offers valuable references for related fields.