基于数据挖掘技术的学生校园消费行为分析

Students consumption behavior analysis based on the data mining technologies

校园一卡通系统通过对各种信息、资源的有效集成、整合和优化,能够实现学校对信息的有效配置和充分利用。文章采用数据挖掘技术针对学生校园消费活动的管理分析方面进行深入研究,首先通过数据预处理技术提取相关消费特征,并采用一种优化的K-means聚类算法,将学生分为几类,分析行为特征,以便高校学生工作人员分门别类的进行学生管理,最后将聚类结果输入决策树分类模型进行评估,以评价聚类结果。

With effective integration and optimization to variety of resources, the campus card system（CCS） makes efficient allocation and full use of campus information. management analysis of students＇ consumption activities is studied through data mining. We use date preprocessing techniques to extract relevant consumer characteristics and an optimized k-means clustering algorithm to divide students into several categories. At last we use the decision tree algorithm to judge the clustering results. The analysis of the behavior characteristics can lead to a better management of students for college staffs.