Study on the Method of Road Transport Management Information Data Mining based on Pruning Eclat Algorithm and MapReduce

摘要

Road transport management information is a class of massive and correlation data in ITS (intelligent transportation systems), and its association rules data mining has important practical significance. In order to cover the shortage of the classical association rules optimized algorithm Eclat, this paper proposed and demonstrated that candidate sets which have the project as a prefix or suffix can be pruning calculated for both the properties. Then it proposed optimized method of frequent sets calculation-a method of parallel NEclat combining with cloud programming model. This method can solve the problem that Eclat algorithm cannot be calculated by pruning, and achieve a parallel compute. The practical application showed that, this method can reduce time waste by more than 40%, and it is suitable for the data mining of transport management information association rules.