Development of Student Biochemical Index Monitoring System Based on K-means Cluster Analysis

Abstract

Data mining is a new type of information processing technology, through the analysis of massive data, to find hidden information, knowledge and trends. Cluster analysis is the most important data mining technology. The K-means algorithm is one of the more classic clustering algorithms, which optimizes the clustering results through step-by-step iteration. It has the advantages of reliable theory, simple implementation and fast convergence. Based on K-means clustering analysis method, this paper constructs a student biochemical index monitoring system, which provides reference for the formulation of students' physical health and development planning. The core work of this study is to build the mathematical model of K-means clustering analysis algorithm, design the conceptual structure and logical structure of the database, and complete the design of the student biochemical index cluster analysis software.