Analysis and classification of employee attrition and absenteeism in industry: A sequential pattern mining-based methodology

Abstract

Employee attrition and absenteeism are major problems that affect many industries and organizations, resulting in diminished productivity, elevated costs, and losses. These phenomena can be attributed to multiple factors that are difficult to anticipate for human resources or management. Therefore, this paper proposes a content-based methodology for the analysis and classification of employee attrition and absenteeism that can be used for talent analysis and management, a task that is traditionally carried out ex-post. The developed methodology, called E(3A)CSPM, is based on SPM (sequential pattern mining). In the methodology, four public datasets with diversified employee data are adopted, which are initially transformed into a suitable format. Then, SPM algorithms are applied to the transformed datasets to reveal recurring patterns and rules of features. The discovered patterns and rules not only offer information regarding features that have a key role in employee attrition and absenteeism but also their values. These frequent patterns of features are thereafter used to classify/predict employee attrition and absenteeism. Eight classifiers and multiple evaluation metrics are used in experiments. The performance of E(3A)CSPM is contrasted with state-of-the-art approaches for employee attrition and absenteeism and the obtained findings reveal that E(3A)CSPM surpasses these approaches.