关联规则挖掘在大学生心理健康测评系统中的应用研究  

The Application of Association Rule Mining in College Students′Mental Health Assessment System

作　　者：亓文娟[1] 晏杰[1] 黄书城 郭磊[1] 卢荣辉[2] 

机构地区：[1]武夷学院数学与计算机学院,福建武夷山354300 [2]武夷学院实验室管理中心,福建武夷山354300

出　　处：《湖南工业大学学报》2013年第6期94-99,共6页Journal of Hunan University of Technology

基　　金：福建省教育厅基金资助项目(JA11268);福建省大学生创新训练计划基金资助项目(201310397022)

摘　　要：为深入了解影响大学生心理健康的主要因素以及心理症状之间的关联关系,将关联规则挖掘应用于大学生心理健康测评数据。先对原始数据进行预处理,再以Clementine 12.0为平台,建立大学生心理多维关联规则挖掘模型;以福建省某高校2011级的学生心理测试数据为基础,应用构建的模型分析了性别、独生子女、来源地、学生干部、家庭结构、家庭月收入6个属性和大学生九维心理症状间的关联关系。根据挖掘结果可以更深入地了解学生心理健康问题,为高校开展大学生心理健康教育的规划、决策提供依据。In order to deeply understand the main factors affecting the psychological health of college students and the correlation between psychological symptoms, the association rule mining is applied to college students＇ psychological health survey data. After preprocess the initial data of college students＇ psychological assessment information, a multidimensional association rule mining model is built based on Clementine 12.0 platform. Taking the psychological test data of the 2011 grade students in a university of Fujian Province as the foundation, the constructed model is used to analyze the correlation between the six attributes （ gender, only-child or not, native place, student cadre or not, family structure and family＇ s monthly income ）and nine-dimensional psychological symptoms. The mining results help to get a deeper understanding of students＇ mental health problems and provide a basis for colleges to make plans and decisions in college students＇ psychological education.

关 键 词：数据挖掘 关联规则 APRIORI算法 CLEMENTINE 心理健康 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]