A High-performance Association Rule Mining Algorithm Based on FP-tree and Its Application in Railway Tunnel Safety Management

FP-growth (Frequent Patterns-growth) algorithm for association rules is the one with relatively good performance in data mining. On the basis of the analysis and the innovation of this algorithm, a new and high-performance algorithm of FP-growthN was built according to FP-tree (Frequent Patterns-tree), which was especially suitable for mining these data with large volume yet sparse items. Then, this new algorithm was applied to mining the association of damages in railway tunnels. The hidden relations among tunnel damage were discovered through the association analysis of valid damage data from 343 out of 2787 tunnels damaged data in 2005, which are ruled over by the Chengdu Railway Bureau. The result obtained by the new algorithm is helpful for the prevention of damages and the establishment of detection criterion in tunnel industry.