关联规则在基于学分制的选课系统中的应用

高校选课系统中存储了大量的数据,利用数据挖掘技术的关联规则挖掘,可以从大量的数据中发现有价值的规则.以高校选课系统为应用背景,对学生成绩进行分析,得出部分合理、可靠的课程相关性规则,为学分制体系下学生选课提供指导.     

高校课程相关性粗糙集分析模型及应用

将目前在数据挖掘领域应用广泛的粗糙集理论引入高校课程相关性分析中,提出了基于粗糙集的高校课程相关性分析模型。该模型首先运用粗糙集的相关性质对参与分析的决策数据进行属性约简,从而获得了较小决策数据集,然后应用基于分辨矩阵的关联规则提取算法提取关联规则,最后对规则进行评估与解释。通过对某高校某专业学生修读课程考试成绩数据进行实际应用分析,发现了课程成绩数据中隐藏的课程相关性规则,分析结果表明该模型在学分制体系下指导学生选课以及制定专业修读计划具有一定的辅助作用。     

面向学分制的计算机应用基础课程体系运行分析

学分制教学管理制度对高等学校计算机应用基础教育提出了新的要求.文章介绍了东华大学面向学分制的计算机应用基础课程体系,通过对学生选课数据汇总和学生问卷调查,分析了该课程体系的运行情况和存在问题,提出了进一步的改革建议和课程建设中应该注意的问题.     

基于关联规则挖掘的高校成绩分析研究

本文通过对本校某年级学生成绩进行分析,主要应用数据挖掘中的关联规则和Apriori算法,挖掘出一些合理的课程关联规则,将这些规则运用到教学管理中,可指导学生选课和合理的设置课程,为高校的教学管理提供参考。     

关于学分制下数字校园建设的思考

学分制已成为我国高校教育教学管理的主要模式,本文以学分制下如何建设数字校园为研究内容,阐述了学分制下数字校园建设的内容——选课系统、学分管理和远程教学,并论述了数字校园建设过程中应注意的问题,包括硬件设施保障、软件系统建设、更新教学观念、学生自主学习,以促进我国学分制下数字校园建设工作的顺利开展。     

如何让高职弹性学分制“弹”起来?

本文针对高职弹性学分制中存在的问题,探讨了高校学分制教学管理模式的构建和高校"弹性学分制"教学管理的实施办法。     

关联规则在高校系统中的应用

运用数据挖掘中的关联规则分析了高校教学管理中教师信息之间的隐藏关系.并对数据进行了标准化处理,采用优化的Appriori算法进行数据挖掘.通过事例分析了教师的教学工作量和发表论文之间的隐含关系,可为教学管理提供决策支持.     

基于改进Apriori算法的高校课程预警规则库构建

在如何构建完善的课程预警规则库是高校成绩预警研究中的一个重点问题, 本文对高校学生成绩进行清洗、离散化后, 利用Apriori算法挖掘不及格课程之间的相关关联, 构建基础预警规则库, 在此基础上进一步挖掘"及格", "良好"等级课程对其他课程的影响, 从而进一步扩充预警规则库. 针对大量冗余规则的情况, 在传统的支持度-置信度框架下利用提升度、兴趣度等方法筛选出强关联规则, 提高规则库的准确度, 并对挖掘出的规则进行了针对性的分析, 研究方法和结论可为教学管理提供决策支持.     

关联规则在教务管理中的应用

运用数据挖掘技术中的关联规则,对历届学生成绩数据进行分析,找出各课程之间的隐藏关系,对数据进行了标准化、离散化处理,并采用经典Apriori算法进行数据挖掘,得到了一些合理、可靠的课程关联规则.这些规则应用到教学管理中,可以为学生选课提供有效的指导以及合理设置课程.     

基于Internet/Intranet的高校综合教学管理系统

教学管理信息化是高校信息化管理的重要组成部分 ,特别是学分制下的信息化教学管理更是学分制管理体制顺利实施的重要保证。本文简述基于Internet Intranet的高校综合教学管理系统结构、功能和系统主要技术     

农村社会保障体系数据流关联规则挖掘

针对我国农村社会保障体系数据流存在的隐含信息,对该体系数据流关联规则挖掘已成为研究的热点。鉴于此,提出农村社会保障体系数据流产生关联规则的几个步骤及相应的实现方法,包括在数据流采样中的置换方法,以及在频繁集生成中的MFI—TCQ方法,介绍关联规则树的产生并举例进行说明。     

关联规则在课程相关性中研究与应用

关联规则挖掘是数据挖掘领域的一个重要课题，本文介绍了在数据挖掘中关联规则的基本概念和与理论，进一步讨论了关联规则在课程相关性挖掘中的应用。学生成绩库在经过一定的预处理后，用Apriori算法挖掘出隐藏在数据背后的有用规则，以指导学生的选课。     

MCRS: A course recommendation system for MOOCs

With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user’s courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.

     

数据挖掘技术在数码产品销售商家的应用

该文介绍了数据挖掘技术在数码产品销售商家中的应用,洪娄底诚信科技公司为例,首先让人对数据挖掘技术建立一个正确的观念．消除对于数据挖掘技术的误区,为后面数据挖掘技术应用打下基础;接着提供数据挖掘技术中类聚分析将客户有效分类,运用关联规则挖掘技术找出业务间的关联性．从而进行货柜商品摆设,针对不同客户进行有效、个性化的营销。     

A new approach for constructing the concept map

In recent years, e-learning system has become more and more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners’ historical testing records. Phase 1 is used to preprocess the testing records; i.e., transform the numeric grade data, refine the testing records, and mine the association rules from input data. Phase 2 is used to transform the mined association rules into prerequisite relationships among learning concepts for creating the concept map. Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic data, apply Education Theory to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also discussed. Moreover, we also develop a prototype system of TP-CMC and then use the real testing records of students in junior high school to evaluate the results. The experimental results show that our proposed approach is workable.     

基于磁盘表存储FP-TREE的关联规则挖掘算法

随着现实待挖掘数据库规模不断增长,系统可使用的内存成为用FP-GROWTH算法进行关联规则挖掘的瓶颈.为了摆脱内存的束缚,对大规模数据库中的数据进行关联规则挖掘,基于磁盘的关联规则挖掘成为重要的研究方向.对此,改进原始的FP-TREE数据结构,提出了一种新颖的基于磁盘表的DTRFP-GROWTH(disk table resident FP-TREE growth)算法.该算法利用磁盘表存储FP-TREE,降低内存使用,在传统FP-GROWTH算法占用过多内存、挖掘工作无法进行时,以独特的磁盘表存储FP-TREE技术,减少内存使用,能够继续完成挖掘工作,适合空间性能优先的场合.不仅如此,该算法还将关联规则挖掘和关系型数据库整合,克服了基于文件系统相关算法效率较低、开发难度较大等问题.在真实数据集上进行了验证实验以及性能分析.实验结果表明,在内存空间有限的情况下,DTRFP-GROWTH算法是一种有效的基于磁盘的关联规则挖掘算法.     

Hybrid genetic algorithm and association rules for mining workflow best practices

Business workflow analysis has become crucial in strategizing how to create competitive edge. Consequently, deriving a series of positively correlated association rules from workflows is essential to identify strong relationships among key business activities. These rules can subsequently, serve as best practices. We have addressed this problem by hybridizing genetic algorithm with association rules. First, we used correlation to replace support-confidence in genetic algorithm to enable dynamic data-driven determination of support and confidence, i.e., use correlation to optimize the derivation of positively correlated association rules. Second, we used correlation as fitness function to support upward closure in association rules (hitherto, association rules support only downward closure). The ability to support upward closure allows derivation of the most specific association rules (business model) from less specific association rules (business meta-model) and generic association rules (reference meta-model). Downward closure allows the opposite. Upward-downward closures allow the manager to drill-down and analyze based on the degree of dependency among business activities. Subsequently, association rules can be used to describe best practices at the model, meta-model and reference meta-model levels with the most general positively dependent association rules as reference meta-model. Experiments are based on an online hotel reservation system.     

最大频繁集的关联规则矩阵视图

文章研究了两个基本的关联规则推导关系,在此基础上建立了最大频繁集的关联规则矩阵视图,把一个频繁集生成的所有规则全部展现在一个矩阵中,并通过研究矩阵中的各规则元素的关系,得到一个频繁集或规则矩阵的基集和核(即最小规则集),可以从大型事务数据库生成的大量关联规则中挖掘出最小规则集和有用户感兴趣的规则。