Text-document clustering-based cause and effect analysis methodology for steel plant incident data.

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.