Factors correlation mining on railway accidents using association rule learning algorithm

Although much research work for the operation safety has been taken in the railway domain, some accidents still occur because past experiences of accident analysis were not fully accumulated for safety improvement. This study aims to identify potential causal relationships among the many factors playing a role in railway accidents. A new interestingness measure, Confidence-interestingness ('C-' Inter) and corresponding improved algorithm, Positive and Negative Association Rules Algorithm based on 'C-' Inter (PNARA-CI) were put forward in our study. Compared with traditional association rule mining algorithms, the PNARA-CI does not generate candidate association rules by means of frequent itemsets, but by the combination between every two accident factors, which can mine the positive and negative association rules with practical value to the maximum. And they were applied to railway accidents data to explore the association rules of the causal factors in the case study. The effectiveness of the algorithm was verified.