Abstract
After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small.
Highlights
With the rapid development of information, more and more data information will inevitably be created
Iris Wine Glass Diabetes Heartstatlog Ionosphere Sonar Average Standard variance, the difference between the two groups was tested by LSD-t, and the statistics of the results of the application of the fuzzy decision tree algorithm in the management of sports and fitness members were performed by the group t test
The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper achieved the highest accuracy rate (86.32%), which shows that the fuzzy decision tree ID3 algorithm based on mobile computing can produce high accuracy when dealing with continuous attributes
Summary
With the rapid development of information, more and more data information will inevitably be created. Chiu et al tried to apply the fuzzy decision tree that combines the advantages of fuzzy theory and fuzzy tree into this field by analyzing the problems in the management of high school teachers. The experimental results show that the method is feasible and effective It provides new ideas for analyzing and predicting customer confiscation crises and helps managers make better decisions in business strategies. In order to explore alternative methods to solve this problem, the application of using an automatic program to generate a soil classifier from data using a fuzzy decision tree induction algorithm is studied. Based on the analysis of the fuzziness of customer data characteristics, this paper compares the two inductive learning algorithms of decision trees and fuzzy decision trees and uses mobile computing to optimize two important parameters involved in the establishment of fuzzy decision trees. Application of Fuzzy Decision Tree Algorithm in the Management of Sports Fitness Members
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