基于聚类算法的教育资助系统的设计与实现

发布时间：2019-06-27 12:27

【摘要】：教育公平是社会公平的重要基础,是国家基本教育方针。通过国家资助帮助家庭经济困难学生,让其接受教育,帮助家庭经济困难学生度过艰难时期,是实现国家长治久安、建设社会主义和谐社会的本质要求。使得在读经济困难学生的经济资助工作便捷、高透明地完成,便成为了一个函待解决的重要问题。论文设计实现基于聚类算法的教育资助系统,将由于操作人员误操作引起的隐患降至最低,可使资助工作人员大大提高工作效率,同时减少运作成本,确保数据真实性,将资助工作彻底公开透明化。现有的K-means聚类算法主要问题在于:产生的噪声过大且中心点的选取上往往会产生局部最优解。本文针对以上问题,引入遗传算法对聚类中心点的选取过程进行优化,并在此基础上提出了基于改进的遗传算法的K均值聚类算法。本文改进的K均值聚类算法一方面采用第一重遗传算法实现对聚类中心的优化,通过遗传种群群体个体的分析来确定遗传中心的个数;另一方面,以个体为中心进行一次聚类,得第二重遗传算法个体适应度,从而确定聚类中心的全局最优解。论文首先通过研究背景及意义的论述说明了设计一款基于聚类算法的教育资助系统的必要性;其次,介绍了聚类算法及关联分析的相关理论,为提出助学金教学资格挖掘所需的数据聚类算法和聚类分类算法提供依据;再次,在需求分析过程中,分析了资助中心基本业务需求、系统功能需求等;此外,针对需求分析,提出了系统的数据库设计、系统详细设计、基于数据聚类算法的聚类算法;最后,针对系统设计展示了教育资助系统实现部,经过测试,系统达到了预期的功能要求。
[Abstract]:Educational equity is an important foundation of social equity and the basic educational policy of the country. It is the essential requirement of realizing the long-term stability of the country and building a harmonious socialist society to help the students with family financial difficulties to get through the difficult times by helping the students with family financial difficulties to receive education through the state financial aid. So that the financial aid work of students with financial difficulties is convenient and highly transparent, which has become an important problem to be solved by correspondence. In this paper, an educational aid system based on clustering algorithm is designed and implemented, which minimizes the hidden dangers caused by operators' misoperation, which can greatly improve the work efficiency of the aided staff, reduce the operating cost, ensure the authenticity of the data, and make the financial work completely open and transparent. The main problem of the existing K-means clustering algorithm is that the noise generated is too large and the selection of the center point often produces the local optimal solution. In order to solve the above problems, genetic algorithm is introduced to optimize the selection process of clustering center, and on this basis, a K-means clustering algorithm based on improved genetic algorithm is proposed. On the one hand, the improved K-means clustering algorithm uses the first genetic algorithm to optimize the clustering center, and the number of genetic centers is determined by the analysis of genetic population individuals; on the other hand, the individual fitness of the second genetic algorithm is obtained by clustering with the individual as the center, so as to determine the global optimal solution of the clustering center. Firstly, the paper explains the necessity of designing an educational aid system based on clustering algorithm through the discussion of research background and significance. Secondly, it introduces the clustering algorithm and the related theory of association analysis, which provides the basis for putting forward the data clustering algorithm and clustering classification algorithm needed for grant teaching qualification mining. Thirdly, in the process of demand analysis, the basic business requirements and system functional requirements of the aid center are analyzed. In addition, for the requirement analysis, the database design of the system, the detailed design of the system and the clustering algorithm based on the data clustering algorithm are put forward. Finally, the implementation department of the educational aid system is displayed for the system design. After testing, the system meets the expected functional requirements.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP311.52

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