计算机应用研究2012，Vol.29Issue(6)：2168-2171,4.DOI:10.3969/j.issn.1001-3695.2012.06.044
基于杂交聚类算法的学生状态分析系统研究
Student status analysis system research based on hybrid clustering algorithm
摘要
Abstract
With the number of students and their information increasing, the original student management and evaluation pattern could not meet demand in college. This paper researched a student status analysis system, which could collect the students' comprehensive data and provide platform to communicate between counsellor and students. In view of the deficiency of global search ability for K-means clustering algorithm, this paper proposed K-means algorithm based on particle swarm optimization to analyze the students' data. Compared with those of K-means algorithm, K-means algorithm based on genetic algorithm and artificial evaluation, the results show the evaluation obtained from the system is more comprehensive and objective. The system could also offer visual information convenient to query, which helped counsellor find sticking point timely and improve efficiency.关键词
学生管理/学生状态评估/粒子群优化算法/K-均值算法Key words
student management/student status evaluation/particle swarm optimization algorithm/K-means algorithm分类
信息技术与安全科学引用本文复制引用
肖立中,胡婷,刘云翔,林振骏,吴雁林..基于杂交聚类算法的学生状态分析系统研究[J].计算机应用研究,2012,29(6):2168-2171,4.基金项目
上海市教育委员会科研创新资助项目(12YZ164) （12YZ164）
上海应用技术学院校内科技发展基金资助项目(KJ2011-03) （KJ2011-03）
上海应用技术学院计算机科学与技术重点学科资助项目(1020Q121002) （1020Q121002）
