Identification of Correlated Factors for Absenteeism of Employees Using Clustering Techniques

Divyajyoti Panda (), Debjani Panda () and Satya Ranjan Dash ()
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Divyajyoti Panda: University of Southern California
Debjani Panda: Indian Oil Corporation Limited
Satya Ranjan Dash: KIIT Deemed-to-be-University

Chapter Chapter 5 in Advances in Data Clustering, 2024, pp 79-93 from Springer

Abstract: Abstract Absenteeism has become one major concern for companies in this digitization era. To sustain in the competitive market and ensure timely delivery of products and services, as per the timeline, an uninterrupted performance of workforce is essential. This chapter focuses on finding out the crucial factors affecting the presence of the employee, and clustering has been used for the classification of absenteeism. The K-means algorithm has been used for determining clusters and critical correlated features have been mapped with a heatmap using a publicly available data set from University of California, Irvine (UCI). Factors related to lifestyle like obesity, higher BMI, employees with higher workload, distance of employee from workplace, etc. have been identified to be the most important factors affecting the presence of the employee at the workplace.

Keywords: K-means; Classification; Clustering; Similarity functions; Heatmap (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-7679-5_5

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