Student status analysis system research based on hybrid clustering algorithm
基于杂交聚类算法的学生状态分析系统研究

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.