Skip to Main Content
Early ID3, C4.5, CART and the other decision tree algorithms are no longer met the situation of massive data analysis for the time being. Those algorithms has the same limitations that they can not handle the updated data sets dynamically and the decision tree generated by these algorithms need to be purned. These weaknesses limit the use of the above-mentioned algorithms. So a novel parallel decision tree classification algorithm based on combination (PCDT) is put forward in this paper. This algorithm has the excellent features that it can be updated when the data set is renewing and it is scalable and no pruning. It has been proved by the experiment that the PCDT algorithm has higher classification accuracy and is easy to parallel.