Construction of University Student Information Management System Based on Constrained Clustering Algorithm

The traditional student information management system can no longer meet the needs of universities for deep information mining and intelligent analysis. In this paper, a solution based on constrained clustering algorithm is proposed, which can introduce external constraints and guide the clustering process according to users' prior knowledge or preferences, so as to obtain more practical clustering results. The system adopts $B / S$ architecture design, which is divided into access layer, function layer, standard layer and basic layer to ensure cross-platform access, ease of use, stability and expansibility. In the aspect of database design, SQL Server 2005 is used to store data, and key table structures such as student information table, achievement table and curriculum table are designed. The core algorithm is constrained K-means clustering algorithm, which introduces constraints on the basis of traditional K-means algorithm, and improves the accuracy and practicability of clustering results. The realization of functional modules includes the input, query, modification and deletion of students' information, as well as performance analysis and behavior pattern recognition based on constrained clustering algorithm. The experimental results show that the system can effectively cluster and analyze students' grades and behavior patterns, accurately identify different student groups, and provide scientific basis for teaching management and student counseling.