Collaborative mining method of traffic accident data based on decision tree and association rules

It is of great significance to scientifically analyze and excavate the internal relationship between traffic accident data, find out the potential laws hidden in the different attributes of the accident data, and thus provide suggestions and theoretical and technical support for the management decisions of relevant departments. In order to overcome the low recall rate, low precision rate and long task completion time in traditional traffic accident data collaborative mining methods, a traffic accident data collaborative mining method using decision tree and association rules is proposed. First of all, use the Map Reduce computing framework to build a traffic accident data collaborative mining framework; Then, combining with the weighted model to improve the association rules, the traffic accident data is preliminarily mined; Finally, the attribute with the largest information gain is calculated, and the traffic accident data is classified using the decision tree to realize the collaborative mining of traffic accident data. The experimental results show that the recall and precision of this method can reach 98.75% and 98.74% respectively, and the mining task completion time is only 0.2s at the fastest, which has good application effect.