Abstract
This study aims to identify main causes of abnormal accidents using datamining techniques based on text data contained in the accident report documents. First, the local outlier factor (LOF) algorithm is applied to discover the abnormal accidents through the keyword vectors extracted from accident report documents. Second, the main causes of abnormal accidents are hierarchically structured using the decision tree (DT) algorithm. As a case study, the chemical industry, which includes various hazardous substances and dangerous facilities, is selected to retrieve features of abnormal accidents and to identify the main causes of abnormal accidents during accident process. Also, we develop the checklist constructed by the main causes of abnormal accidents extracted from the proposed approach according to the process safety indicator. This systematic approach helps safety managers to monitor the abnormal accident compared to the normal accident or near miss.
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