Abstract
The evaluation system of students is to find a way to solve the status way according to the exact needs of students and the teaching requirements of teachers, so as to improve the teaching level of teachers and improve the quality of school education. This paper uses the real evaluation sample and uses the data mining association rule algorithm to comprehensively analyze the massive data of the evaluation data and the basic information of the teacher. The purpose is to obtain the association rules between the teacher’s comprehensive information and its evaluation results. Using the evaluation data to explore its core issues. In this paper, the Eclat algorithm of association rules improves the problem of insufficient memory and occupying a large amount of time when searching for frequent itemsets in the data. The breadth-first algorithm is added to save operation time and improve the efficiency of the algorithm. The effectiveness of the improved algorithm is verified by comparative experiments and applied to the evaluation system so as to provide suggestions for the professional development of teachers from an objective perspective, and to build a harmonious, "people-oriented" evaluation system for students.
Highlights
Eclat algorithm is improved in the optimization and pruning of candidate sets, which reduces the computational complexity
The association rule algorithm is used and fuzzy-element association rules mining algorithm are to further deepen the evaluation data, in order to facilitate real-time compared and summarized, But there is no dedication to analysis and tracking of data, and to enhance the visibility effect of evaluation
In [3], the author's idea of improving the Eclat algorithm is to verify the validity of the evaluation data, and an effective based on the Spark platform, and by parallelizing the data into method for solving the evaluation status is proposed, but did not different computing nodes to achieve parallel computing, to elaborate on improving data mining
Summary
Eclat algorithm is improved in the optimization and pruning of candidate sets, which reduces the computational complexity. Rules from multiple data sets, the idea of meta-association rules is student evaluation is relatively credible and effective in terms of proposed, and the explicit meta-association rules mining algorithm practicality and operability. In [3], the author's idea of improving the Eclat algorithm is to verify the validity of the evaluation data, and an effective based on the Spark platform, and by parallelizing the data into method for solving the evaluation status is proposed, but did not different computing nodes to achieve parallel computing, to elaborate on improving data mining. Literature [4] combines the Eclat At the same time, it is applied to the student evaluation system to algorithm with the data programming model MapReduce to speed find out the problems existing in the evaluation of the status quo up data computational efficiency, but still does not implement the a and to find a solution. Speaking, online evaluation has more advantages [1]: the operation process is simple, the data collection is accurate and fast, which is conducive to analysis and summary, green environmental protection and so on
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