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In the context of the data-driven era, it is essential to explore practical ways to cultivate the core literacy of sports professionals in colleges and universities. This study aims to analyze the core literacy of physical education majors in depth by using data mining technology to seek a more scientific and systematic cultivation strategy. The study used the decision tree CART algorithm and the improved Apriori algorithm to analyze the physical fitness data of the students in the School of Physical Education of S University. The CART algorithm clustered the biological fitness data of male and female students, and it was found that male students’ primary physical fitness deficiencies were concentrated in the upper body strength and standing long jump events. In contrast, female students showed deficiencies in endurance and lower body explosive strength. The improved Apriori algorithm reveals the association rules between different physical fitness items, for example, there is a strong association between boys’ 50-meter running performance and pull-up performance. There are apparent differences in the influencing factors of physical fitness between male and female students, which need to be targeted to design training programs. It was found through association rule mining that specific physical testing programs significantly affect students’ physical fitness quality. This study provides a new path for cultivating core quality of physical education professionals based on data mining, which offers scientific basis and practical guidance for physical education in colleges and universities.

Based on the special nature of language subjects, if the teaching methods adopted by teachers in the language classroom are not appropriate, it is easy to make the classroom atmosphere boring and depressing, which is not conducive to mobilizing students’ learning initiative. The use of digital media technology is more likely to increase students’ enthusiasm for learning and enhance the teaching impact. To address language education’s shortcomings, this paper utilizes digital media technology to enhance language education through media integration. In order to evaluate the teaching effect of integrating digital media in language teaching, this paper combines the Apriori algorithm and C4.5 algorithm to analyze the knowledge and information about the teaching law and teaching situation from a large number of teaching evaluation results, which is used to judge the effect of the application of digital media in language teaching, after comparing the combination algorithm with the traditional rule-mining algorithm. The Apriori algorithm is applied to secondary school language teaching, incorporating digital media for correlation analysis, and the pre-processed dataset is mined and the quality of language teaching in digital media has a great relationship with the quality of teaching and the content of teaching. In this paper, based on the Apriori-C4.5 mining algorithm, we can analyze the factors associated with teaching effectiveness and propose strategies for digital media application in language education innovation.

This paper analyzes the basic customer management process and chooses the Apriori algorithm to build a CRM model based on data mining. In addition, this paper designs a customer relationship management system based on big data. The system is divided into three layers: data source, batch processing, and real-time processing. In the part of constructing the system architecture, this paper adopts the Hadoop platform. The batch processing layer, it has consisted of four parts, which include No SQL database, Oracle database, ETL architecture, and Hadoop platform. This paper gives the logical architecture design diagram. The real-time processing layer mainly includes a real-time decision engine and service bus. The key part of this layer is the real-time decision engine, in the design of which the Bayesian algorithm and product recommendation prediction model are used. Finally, this paper takes K company as an example to demonstrate the model and management system. After applying the analytical model and management system, the sales of K company keep increasing.

With the acceleration of the application of information technology in Colleges and universities in China, the efficiency of higher education is constantly improving. The focus of teaching management in Colleges and universities is to continuously improve the teaching level of colleges and universities, and the key is to strengthen the management of students’ performance. Performance warning is a form of student performance management. In recent years, data mining technology is more and more mature, and its application is also very wide. Many students have applied data mining technology to university management. In this paper, we apply data mining technology to college students’ performance early warning, and use Apriori algorithm in association rules to design and build college students’ performance early warning platform, and select two classes of students as the research object to verify. In this study, we choose the English course scores of two classes as the test data, and define the performance warning, which is based on the score below 60. The results show that six students in class a will be subject to performance warning, while seven students in class B will be subject to performance warning. In addition, the performance early warning platform designed by this method, the early warning accuracy rate is as high as 92.85%, the accuracy rate is high, has certain application advantages.

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events.

The emergence of mental health problems of college students is mainly closely related to a variety of factors, and it is crucial to conduct in-depth research and provide scientific and effective mental health services to maintain the physical and mental health of college students. Under the framework of new qualitative productivity, data mining technology is utilized to obtain mental health education data, item set is set for its dataset, and Apriori algorithm is utilized to define the support degree, confidence degree, and strong association rules. Using the association rule model, the current situation of mental health education in colleges and universities is explored, and the corresponding optimization path is proposed. Among all the itemsets, {Obsessive Compulsive, Anxiety}→{Study Stress} has the highest confidence level, with a value of 0.9031, and its corresponding support level is 0.1888, which means that obsessive-compulsive disorder and anxiety are the most important reasons leading to students’ study stress in order to cultivate students’ healthy psychology.

Purpose The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an improved Apriori algorithm. Design/methodology/approach Combined with the characteristics of the mobile e-commerce, an improved Apriori algorithm was proposed and applied to the recommendation system. This paper makes products that are recommended to consumers valuable by improving the data mining efficiency. Finally, a Taobao online dress shop is used as an example to prove the effectiveness of an improved Apriori algorithm in the mobile e-commerce recommendation system. Findings The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved Apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy. Originality/value The improved Apriori algorithm is applied in the mobile e-commerce recommendation system solving the limitation of the visual interface in a mobile terminal and the mass data that are continuously generated. The proposed recommendation system provides greater prediction accuracy than conventional systems in data mining.

There are a lot of links among monitoring points of airport noise. To mine association rules among these monitoring points is very important in order to predict airport noise scientifically and effectively. Due to the low efficiency of the Apriori algorithm for mining association rules, this paper proposes a new algorithm called 'Adapt to Noise Set of Airport-Apriori (ATNSOA-Apriori)'. According to the characteristics of monitoring data sets of airport noise, this algorithm optimizes the monitoring data to improve the validity of the monitoring data sets and uses arrays to store items to lower the number of traversing database. As a result, the efficiency of mining association rules is improved. Taking the actual noise monitoring data in a domestic airport in China for example, the experimental results show that the ATNSOA - Apriori algorithm can deal with monitoring data sets of airport noise more effectively and mine the useful association rules more quickly. The proposed algorithm, therefore, is of vital significance for predicting the value of monitoring points and evaluating the effectiveness of the value of monitoring points. [PUBLICATION ABSTRACT]