【作者】 张海笑；

【导师】 傅秀芬；

【作者基本信息】 广东工业大学 ， 计算机应用技术， 2005， 硕士

【摘要】 由于数据库中存在着大量数据,因此从数据库中发现有用的信息显得十分重要。数据挖掘技术就是为解决这个问题而产生的。对数据挖掘技术的研究,国内外己经取得了许多令人瞩目的成就,并成功地应用到了许多领域,但在教育领域中的应用并不广泛。 由于我国大众化高等教育的普及,高校学生数量的激增,给高校教学工作带来了诸多新的问题,教师迫切地需要科学地研究分析包括学生成绩在内的各个教学环节中的大量的数据信息,从中获取知识,继而科学地指导教学。本文在分析了数据挖掘技术在高校教学研究中应用的可行性之后,提出了一种以提高教学质量为根本目标的应用数据挖掘分类技术进行高校教学研究的实施方案。 分类是数据挖掘的重要组成部分,它根据类标号已知的数据建立模型,进而使用该模型来预测类标号未知的数据所属的类。常用的分类方法有决策树分类、贝叶斯分类、神经网络分类等,其中决策树方法在可理解度、易训练性、易实施性和通用性等方面优于其他的分类方法。本文选择将决策树分类法应用到高校教学研究中。 根据所提出的实施方案,本文以学生成绩分析为例,完整地实现了数据分类挖掘的全过程,包括:确定数据挖掘对象及目标;以网上在线调查的方式为主采集数据;采用数据集成、数据清理、数据转换、数据消减等数据预处理技术;使用ID3决策树算法生成决策树,并利用事后修剪法对决策树进行修剪;最后由决策树产生分类规则。完成了成绩分析决策树模型的建立。 在本文的研究过程中,作者独立开发了基于决策树ID3算法的决策树分类器,该分类器使用简单,有良好的用户界面,具有数据文件访问、生成决策树、修剪决策树、产生分类规则、保存分类结果等功能。实验结果表明,该分类器的分类效果良好。更多还原

【Abstract】 As there are large amounts of data in the databases, it is very important for us to find the useful information from the database, and the Data Mining technology is an efficient solution to this problem. The research of Data Mining has reached significant achievement and has been applied successfully in many areas. However, successful application of Data Mining in the field of education has not been reported.With popularization of higher education, more and more students come into the university and it brings us many new problems. The teachers want to analyze large numbers of data scientifically, which including student’s result was brought in the process of teaching. They want to obtain great benefits for the education. After analyzing the feasibility of using Data Mining technique in the area of higher education, this paper gives a project to achieve it.Classification is a very important task in Data Mining. It builds a model according to the data whose class labels are known, and then uses this model to predict the classes of the data whose class labels are unknown. There are some famous classified algorithms such as Decision Tree, the Bayes, and the Neural Network. Among them, the Decision Tree exceeds the others in the feature of well understanding, well training and achievable. In this paper, we select the Decision Tree classified method in the application of higher education.According to the project, we accomplish the Data Mining process to analyze the student’s result. The process includes making sure the Data Mining target, collecting the data, preprocessing data, classifying and generating the classification rule. We use ID3 algorithms to generate a decision tree, use postpruning method to pruning the tree. And then according to the decision tree, we obtain the classification rule.In the course of researching, we accomplish a Decision Tree classifier. The classifier is simple but it has a nice interface. It can open data file, generate Decision Tree, prune the branch, create the classification rule, and save the result. The experiment shows that the classifier can successfully build decision trees and has a更多还原