利用K-means聚类分析技术分析学生成绩.pdfVIP

学 海 无 涯 利用K-means 聚类分析技术分析学生成绩 摘要：数据挖掘是在海量的数据中寻找模式或规则的过程。数据聚类则是数据挖掘中 的一项重要技术，就是将数据对象划分到不同的类或者簇中，使得属于同簇的数据对象相似 性尽量大，而不同簇的数据对象相异性尽量大。 目前数据挖掘技术在商业、金融业等方面都得到了广泛的应用，而在教育领域的应用较 少，随着高校招生规模的扩大，在校学生成绩分布越来越复杂，除了传统成绩分析得到的一 些结论外，还有一些不易发现的信息隐含其中，因而把数据挖掘技术引入到学生成绩分析中， 有利于针对性地提高教学质量。本论文就是运用数据挖掘中的聚类分析学生成绩的，利用学 生在选择专业前的各主要学科的成绩构成，对数据进行选择，预处理，挖掘分析等。运用聚 类算法分析学生对哪个专业的强弱选择，从而为具有不同成绩特征的同学在专业选择及分专 业后如何开展学习提供一定的参考意见。 关键字：数据挖掘 聚类分析 学生成绩分析 Abstract ：Data mining is a process that in the vast amounts of data looking for patterns or rules. Data clustering is an important data mining technology for people to understand and explore the inherent relationship between things. Clustering is to partition data objects into different categories, or clusters, making the similarity with the clusters of data as large as possible. While the dissimilarity of different clusters of data as large as possible. Nowadays data mining technology is widely used in business and finance. But it is less used in education field. With the increase of enrollment in universities, there are more and more students in campus, and that makes it more and more complex in the distribution of students records. Besides some conclusions from traditional record analysis, a lot of potential information cannot be founded. Importing the data mining technology to students record analyzing makes it more convenient and improve the teaching quality. In this paper, clustering technique in data mining is used to students performance analysis, the use of data structure of main subject before the students specialized in choice of mode, pretreatment and data mining. Using clustering technology to analyse which professional students are good at, so as to choose how to learn professional and give som