Text clustering-based cause and effect analysis

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.

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