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At present, with the development of computer technology, more and more data mining technology is introduced into the education of Physical Education Students. Based on the Apriori algorithm, this paper studies the data mining of education technical ability training for students of physical education major. In this paper, we can dig out the rules that we are really interested in by introducing the lift_measure interest measurement. Meanwhile, aiming at the characteristics of mutual exclusion in data mining, it improves the shortcomings of classical Apriori algorithm, and improves the efficiency of mining frequent itemsets by Apriori algorithm. The improved AD-apriori algorithm can reduce the time complexity and space complexity of the mining process. [ABSTRACT FROM AUTHOR]

Nowadays, the universal application and development of computer technology make data mining play an extremely crucial role in students' English education. In this paper, students' English learning is taken as an entry point, and Apriori algorithm is used to analyze English majors' education technical to train data mining. In this paper, the introduction of lift-measure interest independent is used to explore the rules that can arouse our own interest. Taking the mutual exclusion of mining data into account, optimize and improve the classic Apriori algorithm, it improves the efficiency of mining frequent item sets effectively. After optimization, AD-apriori algorithm can make the reduction in complexity of time and space in mining process. [ABSTRACT FROM AUTHOR]

Nowadays, the common application and development of computer technology makes data mining technology play an extremely crucial role in students' English education. In this paper, students' English learning is taken as an entry point and analyze education technology of English major students on training data mining based on Apriori algorithm. This paper excavates rules that may arouse our interest by introducing the independent method of lift-measure interest. In order to improve the efficiency of classical Apriori algorithm and improve the efficiency of Apriori algorithm mining frequent item sets effectively considering the exclusiveness characteristics contained in the mining data, the optimized AD-apriori algorithm may realize complexity of the mining process in time and space. [ABSTRACT FROM AUTHOR]

Life-satisfaction and loneliness are two key indicators of individual mental state, and their detailed analysis could help improve the resettlement policy of retired athletes. This paper proposes a tree-based frequent itemsets mining method to estimate the influence factors of the life-satisfaction and the loneliness of retired athletes. The basic situations of the retired athletes are collected by the questionnaires and transformed into the binary attributes. Then, an extend prefix tree is built for mining the frequent itemsets. The lift measure is employed to generate the association rules based on the obtained frequent itemsets and realize the rules prune. The actual survey data of 750 Chinese retired athletes are adopted for comparing the proposed method and the Apriori algorithm. Experimental results verify the effectiveness of the proposed method is higher. Moreover, the obtained rules show that the health condition, the education, the social insurance participation affect both the life-satisfaction and the loneliness of retired athletes, and the income only affect the life-satisfaction of retired athletes. [ABSTRACT FROM AUTHOR]

Frequent itemset mining is an essential step in the process of association rule mining. Conventional approaches for mining frequent itemsets in big data era encounter significant challenges when computing power and memory space are limited. This paper proposes an efficient distributed frequent itemset mining algorithm (DFIMA) which can significantly reduce the amount of candidate itemsets by applying a matrix-based pruning approach. The proposed algorithm has been implemented using Spark to further improve the efficiency of iterative computation. Numeric experiment results using standard benchmark datasets by comparing the proposed algorithm with the existing algorithm, parallel FP-growth, show that DFIMA has better efficiency and scalability. In addition, a case study has been carried out to validate the feasibility of DFIMA. [ABSTRACT FROM AUTHOR]

In the field of image processing, data mining technique is being implemented in various concepts. Generally, the management of video content with data mining technique became an essential part since there is an increase in the advancement of multimedia and networking technology. Previously, there are certain algorithm such as Apriori and frequency pattern growth algorithm for video management. In this paper, a novel fast frequency pattern algorithm is designed to find the high priority pattern with minimum time. In this concept the data mining process is carried out in vertical format in order to find the pattern with high priority. The simulated results are compared with the existing data mining algorithms and it is found that the proposed algorithm is efficient in aspect of time and memory size. [ABSTRACT FROM AUTHOR]

The use of data mining technology to analyze college students' scores can effectively analyze and deal with the data of grades, so as to improve the working time limitation of the education administrators and help them to adjust education decision instantly. Using data mining techniques to analyze CET-4 grades, we can find the rules or patterns hidden in the grade data and mine the multiple relationships hidden in the grade data. This article focuses on the association rules and classification techniques, Apriori algorithm and decision tree in association rules applied to college students CET-4 score analysis to mining and analysis of the final scores of college English in four semesters and CET-4 scores relevance and the influence of CET-4 exam four parts (listening, reading, writing and synthesis) on the total score of CET-4, to provide policy makers with decision-making data to further improve college English teaching. [ABSTRACT FROM AUTHOR]

Maritime safety is of paramount significance for marine industry since the maritime accidents may adversely affect the human, cargos, ships and the marine environment in various forms and degree of extent. The study aims to identify potential causal relationships among the many factors that play a role in maritime accidents. Correspondingly, association rule learning is selected as analysis approach, because of its utility in obtaining association rules through data mining on maritime accidents data. Based on the analysis of association rule learning, this study designs the association rules learning procedure of maritime accidents and establishes the association rule learning model of maritime accidents. The novelty of this study is to present a different perspective during maritime accident analysis in which potential causal relationships among the many factors are revealed. Association rule learning of maritime accidents data is carried out based on the Apriori algorithm, and the strong association rules among the causal factors of the accident are generated. The study then analyzed the generated strong association rules to find the potential relationship among the causal factors, and puts forward the coping strategies to prevent similar maritime accidents occurrence. [ABSTRACT FROM AUTHOR]

As the development of network became more complex, protocol reverse engineering has attracted increasing attention and widely applied in intrusion detection, vulnerability discovery, and electronic countermeasures. To separate the obtained binary data frames under complex wireless network environment so as to provide prerequisite for the following reverse protocol analysis, cluster system of complex protocol suites was implemented. First, AC algorithm was utilized to mine the frequent sequence characteristics in binary data frames. Then Apriori algorithm was employed innovatively to analyze the association relationships between these characteristics. In addition, combining with the features of binary frames, the results were conducted by four-step pruning. Finally, the selected characteristics were applied for cluster by the improved K-means algorithm. Results indicated that: the clustering effect of the system for binary protocol data frames is favorable. Meanwhile, as for the multilayer protocol suites with TYPE fields, the system is able to further distinguish the hierarchical relations between multiple protocols. [ABSTRACT FROM AUTHOR]