Abstract
Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools and methods, in response to the needs of the student information management system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions.
Highlights
Data mining started with the research of Knowledge Discovery in Database (KDD)
In response to the above problems, we propose applying the data mining method to the student information management system and extracting useful student information through data mining
Is paper systematically summarizes cluster analysis, one of the key technologies in data mining, conducts indepth research on cluster analysis, introduces the current research hot issue—genetic algorithm optimization into cluster analysis—and proposes a fuzzy genetic algorithm clustering; the main content includes the following aspects: on the basis of a brief introduction to the research background of the subject and the significance of the topic, the current status of education informatization and data mining, related concepts of cluster analysis, and clustering are introduced. is paper introduces the current research status of education informatization and data mining, representative clustering algorithms, introduces traditional clustering based on genetic algorithm and clustering based on fuzzy genetic, and designs and implements a student information management system
Summary
Data mining started with the research of Knowledge Discovery in Database (KDD). It is a key step in the process of knowledge discovery [1]. Data mining is an extremely young and active research field, which combines the latest research results of database technology, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented methods, information retrieval, high-performance computing, and data visualization. Is paper introduces the current research status of education informatization and data mining, representative clustering algorithms, introduces traditional clustering based on genetic algorithm and clustering based on fuzzy genetic, and designs and implements a student information management system. Digitization of the environment (including equipment and classrooms), resources (such as diagrams, handouts, courseware, and information), and activities (including teaching, learning, management, service, and office), thereby, enhances the efficiency of traditional campuses and expands the traditional function of the campus, realizing the comprehensive informatization of the education process and achieving the purpose of improving the quality of teaching, scientific research, and management [9]. In a high-dimensional space, especially considering that such data may be highly skewed and extremely sparse, clustering is extremely difficult. (5) Research on the ability to deal with noisy data: in real applications, most of the data contains, in addition to outliers, unknown data, vacancies, or erroneous data, and some clustering algorithms are sensitive to such data and will lead to lowquality clustering results, so the processing of noise is extremely important. (6) Fuzzy clustering research, such as the clustering of information such as text, image, and sound [10]
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