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With the continuous progress of society and economy, more and more attention has been paid to the physical quality of students. As an essential support carrier, physical education (PE) class is responsible for the exercise and improvement of the physical quality and fitness of students. In this paper, with physical training as a breakthrough point, the data on training quality are evaluated based on the Apriori algorithm and the factor mining is carried out accordingly. Through the Apriori algorithm, an in-depth analysis is carried out in this paper on the practical teaching status of physical training programs at colleges and universities in the country. From various perspectives such as the development of the existing physical training programs and their functional features, the advantages of physical training programs in teaching at colleges and universities are analyzed. At the same time, suggestions are given with regard to the current teaching model and status quo at colleges and universities to achieve the goal of driving the development of physical training in the country. Finally, the results of the practical case analysis indicate that physical training programs are feasible in the education curriculum at colleges and universities. To some extent, the spirit of physical training programs is similar to the concept of physical education at colleges and universities. There are many types of physical training programs, which can be used as rich resources for physical education at colleges and universities. From the results of the research, it can be seen that the Apriori algorithm can be used to evaluate the training quality of physical education effectively; the mastery levels of physical education skills among male students are generally higher than those among female students, and the difference is relatively significant; the algorithm proposed in this paper can be used to evaluate the data effectively, which is of great significance for supporting the decisions on improving the quality of training. [ABSTRACT FROM AUTHOR]

In recent years, China has been paying more and more attention to the development of the sports industry, and many sports are no longer seen as a mere sport, but can be developed into an industry and play an important role in the development of the economy. This paper examines the application of multi-source big data mining techniques in the analysis of basketball economic management. Firstly, through multi-source big data mining technology, we collect various factors that influence the development of basketball economic industrialization, use Hash Tree-based Apriori algorithm to mine various influencing factors for basketball economic industrialization, and analyze the correlation between each influencing factor. The association rule mining results are then used to analyze the relationship between the key influencing factors and the industrialization of the basketball economy. This paper examines various aspects of the Chinese basketball league market, including the management system, market operations, and talent flow, and compares them with the foreign basketball industry models, in order to analyze the operation of China's basketball industrialization and develop corresponding countermeasures to improve basketball economic management based on the results of the study. [ABSTRACT FROM AUTHOR]

This paper takes the physical fitness test data and the physical health self-assessment data as the research objects. The decision tree algorithm is used to construct a decision tree model for students who fail to meet the physical test. Thus, the classification of students with different physical qualities is realized. The association rule Apriori algorithm is used to mine the association of physical fitness test indexes so as to judge the hidden law between students' physical fitness and behavior habits and get the correlation information of various physical health indexes. The back propagation (BP) neural network algorithm is used to establish the physical fitness test prediction model. By using these data mining models, this paper explores the hidden association information in college students' physical test data, which can provide more scientific and effective guidance for students' physical tests. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology and the arrival of the era of big data, the rapid expansion of network information resources has formed massive information. Massive information resources have brought great convenience to people's lives. However, it becomes more and more difficult to find the content that interests you, which is the phenomenon of "information overload." In order to solve this problem, a solution based on personalized recommendation technology is proposed. In personalized recommendation technology, collaborative filtering algorithm is the most widely used technology. Clustering technology can effectively divide objects into groups, so that the similarity of attributes between objects in the same group is high, and the similarity of objects in different groups is low. The core step of the filtering recommendation algorithm is to find the similar neighbors of the target user by calculating the similarity. Applying the clustering technology to the recommendation can effectively improve the performance of the recommendation system. Aiming at the real-time problem of collaborative filtering recommendation, this paper introduces a method of firstly clustering users on the user item rating matrix, and finding the nearest neighbors in the clusters with high similarity with the target user, which effectively reduces the query space and improves the recommendation. This paper proposes a method to measure the user's preference for item attributes, which is used in the above clustering process to improve the recommendation accuracy while retaining the advantage of reducing the query space. Aiming at the problem of poor recommendation accuracy, this paper proposes a fuzzy-improved K-means algorithm to cluster items in the product attribute matrix, and then fuses the similarity of the belongingness of items to clusters in the fuzzy clustering. The similarity calculated on the score matrix shows that this method is better than the traditional hybrid recommendation in accuracy. [ABSTRACT FROM AUTHOR]

The starting point of this study is to improve the realistic problem of high failure rate of product innovation and improve the success rate of product innovation. First, starting from the relationship between user demand and innovation, this study constructs a product innovation-demand screening system model from a macro perspective and describes the circular promotion relationship between demand and innovation. Second, apply computational thinking to further characterize the system model. From the point of view of mining frequent item sets, this paper constructs an analysis model of product innovation intention from a microscopic perspective and makes a correlation analysis of innovation and demand, providing technical support for the combination and screening of innovation and demand. The model runs in a Python environment. This study uses the Apriori algorithm to improve the efficiency of frequent set mining. This model has no specific restrictions on the selected users, the way to select demand data is broad, and the data are easy to obtain. The use of large amounts of data for analysis also reduces the bias of experts and leading users in evaluating and screening innovation intentions. The simulation of the operation and screening efficiency of the product innovation intention analysis model shows that (1) the model runs effectively. (2) The number of innovation intentions screened by the model can be regulated by adjusting only two variables: min_support and min_confidence. However, the number of strong association rules is more sensitive to the adjustment of min_support. (3) The innovation intention analysis model has significantly improved the efficiency of innovation intention screening. (4) Adjusting the innovation portfolio according to the analysis results of innovation intention can make new products more popular. [ABSTRACT FROM AUTHOR]

Around the world, road traffic accidents are the leading cause of serious injuries and deaths. Ethiopia is one of the countries that suffer the most from traffic accidents. Every government in every country wants to keep its citizens safe from accidents. To keep people safe from accidents, it is necessary to conduct a detailed analysis of the factors that contribute to high-severity accidents and deaths. As a result, we developed a data mining algorithm-based road traffic accident severity analysis for the Addis Ababa subcity in this study. The longest frequent factors in the dataset were generated using the Apriori algorithm. The Apriori algorithm generates the most frequent factors as sex: male, driver–vehicle relationship: employee, weather condition: normal, pedestrian movement: not a pedestrian, road surface type: asphalt, and accident severity: high severity, with 42.21% and 84.35% support and confidence, respectively. In addition, we created an accident severity level predictive model using a support vector machine. The predictive model has an accuracy of 85%. The proposed predictive model outperforms other well-known predictive models, such as K-nearest neighbors, decision trees, and random forests. As a result, when making decisions or policies in Ethiopia, the government or private organizations should consider the association of factors that lead to serious severe accidents. [ABSTRACT FROM AUTHOR]

Aiming at the common problem of low learning effect in single structure learning of a Bayesian network, a new algorithm EF-BNSL integrating ensemble learning and frequent item mining is proposed. Firstly, the sample set is obtained by sampling the original dataset using Bootstrap, which is mined using the Apriori algorithm to derive the maximum frequent items and association rules so that the black and white list can be determined. Secondly, considering that there may be wrong edges in the black and white list, the black and white list is used as the penalty term of the BDeu score and the initial network is obtained from the hill climbing algorithm. Finally, repeat the above steps 10 times to obtain 10 initial networks. The 10 initial networks were integrated and learned by the integrated strategy function to obtain the final Bayesian network. Experiments were carried out on six standard networks to calculate F 1 score and H D. The results show that the EF-BNSL algorithm can effectively improve F 1 score, reduce H D , and learn the network structure that is closer to the real network. [ABSTRACT FROM AUTHOR]