Citation

Verma A, Maiti J. Int. J. Inj. Control Safe. Promot. 2018; 25(4): 416-426.

Affiliation

Department of Industrial and Systems Engineering , Indian Institute of Technology Kharagpur , West Bengal , India.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/17457300.2018.1456468

PMID

29629618

Abstract

The purpose of this study is to develop a text clustering-based cause and effect analysis methodology for incident data to unfold the root causes behind the incidents. A cause-effect diagram is usually prepared by using experts' knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm.

RESULTS suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. The study also helped in finding out the anomaly in incident reporting.

Language: en

Keywords

Cause and effect; clustering analysis; incident data; root causes; text mining