What does this research mean for the field?

K-means clustering and dense neural networks are highly effective methodologies for segmenting employees and predicting future skill requirements, respectively, enabling proactive talent management in human resources. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to address challenges in human resource management by utilizing clustering and deep learning for workforce analysis and skill prediction.

June 1, 2026

Knowledge-centric approaches in human resource management: leveraging clustering and deep learning

Key Points

This research aims to address challenges in human resource management by utilizing clustering and deep learning for workforce analysis and skill prediction.
Analyzed workforce dynamics through clustering to segment employees based on attributes.
Evaluated multiple clustering algorithms including K-means, with K-means being the most effective with a Silhouette Score of 0.874156.
Predicted future skill requirements using a dense neural network that achieved the lowest mean squared error of 4478.58.
K-means clustering achieved a Silhouette Score of 0.874156 and a Davies-Bouldin score of 1.285476.
The dense neural network model showed the lowest mean squared error of 4478.58 and an R2 score of 0.94.
Feature importance analysis from the dense neural network highlighted key predictive factors for HR practitioners.

Abstract

This research tackles contemporary human resource management challenges using advanced analytics methodologies. Initially, workforce dynamics are analysed through clustering to segment employees based on attributes. Among several algorithms evaluated, including K-means, agglomerative clustering, spectral clustering, and Gaussian mixture models, K-means proves most effective, with a Silhouette Score of 0.874156 and a Davies-Bouldin score of 1.285476. The study then predicts future skill requirements using deep learning models, focusing on the dense neural network. The dense NN emerges as the top predictive model, with the lowest mean squared error of 4478.58, the lowest mean absolute error of 47.56, and the highest R2 score of 0.94. Additionally, feature importance analysis highlights the dense NN's ability to capture intricate relationships, aiding HR practitioners in understanding key predictive factors. This research equips HR professionals with critical insights for proactive talent management and workforce planning.

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