一种基于FP_Tree算法的决策树构造方法

Construction Method for Decision Tree Based on FP_Tree Algorithm

针对大规模训练元组决策树构造效率较低的问题,提出一种改进的决策树构造方法。该方法利用FP_Tree算法,比采用经典Apriori算法节省了更多内存开销。使用FP_Tree路径替代经典算法中训练元组的分裂计算,得到与原算法相同的决策树模型。实验结果证明,改进后的方法具有良好性能。

Aiming at the low efficiency problem of the construction of decision tree in large-scale training units, this paper presents an improved construction method for decision tree. This method uses FP Tree algorithm to save more memory than Apriori algorithm. It takes the place of split algorithm of the training units in classical algorithm by the path of FP_Tree, and gets the same decision tree model as the original algorithm. Test results show that the improved method has good property.