Earticle

초록

영어

This study proposes an early-warning framework for attrition risk that combines unsupervised clustering (K-means, Gaussian Mixture Model) with survival analysis (Kaplan–Meier, Cox proportional hazards), using data on 11,090 wage workers from the Korean Labor and Income Panel Study (KLIPS, 2002– 2021). After segmenting latent heterogeneity with K-means and GMM, we estimated cluster-specific survival functions and risk factors via KM survival curves and Cox regression. Based on internal and model-based criteria (silhouette, Calinski–Harabasz, BIC, ARI), performance was optimal when the number of clusters was set to five, with a silhouette coefficient of 0.247, a Calinski– Harabasz index of 680.3, the lowest BIC, and an ARI of 0.94. The 24-month cumulative attrition probability for Cluster 2 (low satisfaction and overwork) was approximately 50 percent (highest), whereas Cluster 1 (high satisfaction and stable) was approximately 12 percent (lowest), with a significant difference by the log-rank test, with the p-value below 0.001. In the Cox analysis, Cluster 2 showed a hazard ratio (HR) of about 2.0, and declines in job satisfaction and increases in overtime hours emerged as significant risk factors. The proposed framework provides quantitative grounds for targeted interventions in high-risk segments by means of early-warning indicators based on 12- and 24-month attrition probabilities and lift.

목차

Abstract
I. INTRODUCTION
II. BACKGROUND
III. CONCLUSION
REFERENCES

키워드

저자

• Soonho Jang [ Dept. of Data Science Graduate School, Dankook University Yongin, South Korea ]
• Eung‑Kyo Suh [ Dept. of Metaverse Convergence Graduate School, Dankook University Yongin, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004