An Analytical Framework for Employee Promotion Clustering

Abstract

Choosing which employees to promote is a complex task that demands both fairness and effectiveness. Machine learning has significantly improved promotion decisions by providing data-driven insights and automation. Clustering-based promotion models are popular, but their performance is often hindered by deficiencies in the input data, which are typically noisy, class imbalanced, and high dimensional. These problems can be alleviated through data preprocessing. Accordingly, this study introduces an analytical framework for employee promotion clustering that incorporates feature engineering – both feature extraction and feature augmentation – to enhance clustering performance and generalizability. The principal contribution is the development of the generative performance feature (GPF), an augmented representation that amplifies the influence of performance-oriented features extracted via principal component analysis (PCA). The GPF captures the intrinsic structure of the original dataset and is formulated as an additive composite feature. The PCA-transformed dataset combined with the GPF is then used to construct a clustering model. The proposed framework was evaluated on two public datasets with both K-means and fuzzy C-means (FCM) clustering models. The GPF led to significant improvements across key evaluation metrics, namely, the Rand index, the mutual information score, the V-measure, and the Fowlkes–Mallows index. K-means clustering demonstrated superior performance to FCM clustering across all evaluation metrics for both datasets.