Research on the application of data mining algorithm based on decision tree

In this paper, the author researches on the application of data mining algorithm based on decision tree. Compared with other classification methods, decision tree has the following advantages: relatively smaller calculation workload, the ease of getting apparent rules, the capability of showing important decision characteristics and higher correctness of classification, etc. However, the existing decision tree algorithm also exists a lot of shortage when being applied in practice, such as its lower computation efficiency and bigger scale of decision tree, etc. Attribute reduction algorithm ER based on the degree of dependency of attribute and post-pruning algorithm Prune based on rough set theory are proposed. Finally, the optimized decision tree algorithm is used in supplier measurement system, and its validity is verified when comparing with other algorithm.