The unpredictable structure of risk chains using association rule mining
Yusuke Makino, Kazuhiko Kato, Shigeaki Tanimoto · 2017
In order to control risks and facilitate effective decision-making, the relations among risk chains should be systematically analyzed, which is a very difficult process. The aim of this research is to understand the connective generating structure of risk chains and plan problem solving accordingly. Therefore, in order to select the analysis method, association rule mining was applied. From the extracted data, the height of the sources of a risk chain and recurrence nature of a risk could be discovered. It is expected that these results can prevent the occurrence of risk chains caused by human factors.