What is it about?
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. The text data were analysed using singular value decomposition and expectation-maximization algorithm.
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Why is it important?
The significant finding from this study is that text-document clustering analysis is competent of providing a higher level of detail regarding how and why the incident happened.
Perspectives
Overall, this research has investigated and validated a novel text mining methodology for occupational accident analysis and prevention.
Mr. Abhishek Verma
Indian Institute of Technology Kharagpur
Read the Original
This page is a summary of: Text-document clustering-based cause and effect analysis methodology for steel plant incident data, International Journal of Injury Control and Safety Promotion, April 2018, Taylor & Francis,
DOI: 10.1080/17457300.2018.1456468.
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