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基于数据挖掘技术的网络教学决策支持系统研究-计算机技术专业论文.docx

发布:2019-03-28约6.16万字共81页下载文档
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摘要 现代远程教育的过程主要通过远程教学平台进行,学生在 Web 上的行为都会 被记录,形成巨量的日志,通过数据挖掘分析这些日志,能帮助远程教学平台向 用户提供个性化访问。 论文对 Web 内容挖掘、Web 结构挖掘和 Web 使用挖掘进行了研究,分析了 Web 使用挖掘在教学平台的一般过程及方法,着重讨论了关联规则挖掘和模糊聚 类挖掘方法。分析了传统关联规则挖掘算法的不足,对 Apriori 关联规则方法作了 重点阐述,给出了算法的实现,给出了网络教学决策支持系统的需求分析和设计。 并给出了利用关联规则和模糊聚类实现挖掘的过程,包括数据收集、数据净化、 用户识别、会话识别、模式识别、模式分析。实现了网络教学决策支持系统的关 联规则和聚类分析模块,并给出了系统测试。 关键词:数据挖掘 远程教育 关联规则 知识发现 个性化学习系统 Abstract The process of modern distance education is mainly implemented by means of the distance teaching platform. Students’ behaviors are recorded on Web, which will result in a huge amount of logs. The analyses of these logs by data mining can better the individualized access for users to the distance teaching platform. Researches have been done on content mining, structure mining, and application mining on Web as well as the general process and approaches in Web data mining, focusing on the interrelated rule mining and fuzzy clustering mining. The author has analyzed the disadvantages of traditional interrelated rule mining algorithms, and given the implementation of algorithms, along with the demand analyses and designs for the network instruction decision-making system, emphasizing on Apriori relational analysis method. In this thesis the author has detailed the mining process by utilizing interrelated rules and fuzzy clustering mining, including data collection, data purification, subscriber identification, dialogue recognition, pattern recognition, and pattern analysis. The association rule and clustering analysis modules for the network instruction decision-making system have been realized and the system test has been completed. Key Word:Data mining Distance education Association rules KDD personalized study system 目录 HYPERLINK \l _bookmark0 第一章 绪论1 HYPERLINK \l _bookmark0 1.1 研究背景 1 HYPERLINK \l _bookmark0 1.1.1 现代远程教育概述 1 HYPERLINK \l _bookmark1 1.1.2 现代远程教育对教学模式的改变 2 HYPERLINK \l _bookmark2 1.1.3 电大现代远程教育对教学模式的实践与不足 4 HYPERLINK \l _bookmark3 1.1.4 数据挖掘技术的应用现状 7
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