Study of data mining algorithm based on decision tree

Decision tree algorithm is a kind of data mining model to make induction learning algorithm based on examples. It is easy to extract display rule, has smaller computation amount, and could display important decision property and own higher classification precision. For the study of data mining algorithm based on decision tree, this article put forward specific solution for the problems of property value vacancy, multiple-valued property selection, property selection criteria, propose to introduce weighted and simplified entropy into decision tree algorithm so as to achieve the improvement of ID3 algorithm. The experimental results show that the improved algorithm is better than widely used ID3 algorithm at present on overall performance.