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

Authors: Sumit Tripathi; Roma Trigunait

Addresses: Goa Institute of Management, Poriem, Sattari, Sanquelim, Goa 403505, India ' Babasaheb Bhim Rao Ambedkar University, Lucknow, Uttar Pradesh 226025, India

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.

Keywords: human resource management; HRM; clustering; employee skills; predictive analytics.

DOI: 10.1504/IJKMS.2026.153876

International Journal of Knowledge Management Studies, 2026 Vol.17 No.2, pp.227 - 256

Received: 15 May 2024
Accepted: 11 Dec 2024
Published online: 29 May 2026 *

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