Design of ID3 Decision Tree Algorithm In Network Data Public Management System

Data mining in network data public management system refers to the process of automatically extracting useful information from large-scale network data by using certain methods and tools, so as to provide decision support for network managers. Data mining mainly includes data preprocessing, classification, clustering analysis and so on. Decision tree is one of the most commonly used algorithms in data mining. ID3 decision tree is a decision tree classification algorithm based on information gain theory, which has been widely used in bioinformatics, economics, finance and statistics. Firstly, the information gain is used to calculate the contribution of the eigenvalues of the data set to the classification results. Then, the data set is sorted and segmented by information gain. Finally, the attribute value with the largest information gain is used as the classification result. The ID3 decision tree algorithm does not need to know the eigenvalue distribution of all attribute values in the data set in advance. By classifying each record in the original data set, the probability distribution of the eigenvalue distribution of large samples can be inferred from a small sample. The ID3 decision tree algorithm is simple and the amount of calculation is small, but it is easily affected by the data missing in the data preprocessing process, so the ID3 algorithm cannot be directly applied to the data mining system. Aiming at the shortcomings of ID3 decision tree algorithm, this paper proposes an improved ID3 decision tree algorithm, and applies the algorithm to the network data public management system to realize the efficient storage and retrieval of network data.