Stratified decision tree constructing method

Abstract

The invention relates to a stratified decision tree constructing method comprising the following steps: 1. calculating related coefficients of non-categorical attributes and categorical attributes; 2. taking out attributes with a related coefficient smaller than a thresh value to put in a stratified attribute cluster to be selected; 3. taking out one attribute and stratifying a data set according to the attribute value; 4. recalculating the related coefficients of all the attributes in data subsets, judging whether the attributes are real stratified attributes according to the related coefficient lifting condition of the attributes; 5. repeating the steps 3 and 4 to obtain a real stratified attribute cluster; 6. sequencing according to contribution degree in the stratified attribute cluster; 7. sequentially taking out the stratified attributes, carrying out data set stratification to form a stratified decision tree; and 8. nesting and applying a known excavation method in the data subsets to obtain a complete stratified decision tree. By introduction of stratified attributes, the stratified decision tree constructing method solves the problem that an ideal effect is difficult to obtain by the traditional method under the condition of nonlinear condition attribute relationship; moreover the stratified decision tree constructing method has stronger flexibility.

Description

technical field [0001] The present invention relates to a new data mining method—a method for constructing a stratified decision tree (Stratified Decision Tree, referred to as SDT), which belongs to the field of data mining. Background technique [0002] The decision tree method is one of the most widely used and practical data mining prediction techniques. The principle of the decision tree method is to find out the splitting attribute with the most ability to distinguish different data in the current data set, and then divide the data according to the value of the splitting attribute. The set is divided into multiple subsets, each subset corresponds to a branch of the bifurcation tree, and then this process is called recursively for each subset until all subsets contain the same type of data, thus obtaining a decision tree model, and Based on this, the new data set to be processed is predicted and classified. [0003] Among the decision tree methods, the selection method ...

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Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the existing decision tree method is difficult to achieve ideal results in the presence of nonlinear conditional attribute relations in view of the deficiencies in the prior art, and propose a new decision tree construction method for predicting data Classification and Data Mining

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