The Application Research Of Data Mining In Undergraduate Employment Information Management

With the increasing number of college graduates and more serious employment situation, it becomes more and more urgent for the colleges to design a higher qualified employment information platform. However, designing and implementing this kind of platform is complex system engineering, because the scale of college employment information base is very large and it is increaseing year by year. Furthermore, data in the base have complex structure and are updating fastly. Meanwhile, traditional information management strategies are not suitable for the constant changes of employment situation and the rapid development of society. Data mining technologies can satisfy the requirement of modern scientific management and advanced computer technologies. Data mining can deal with very large scale data set and can discover useful knowledge hidden in data set, which can assist managers to make decisions. Consequently, data mining technologies are employed to manage employment information resources.The major contributions of this thesis are as follows:(1) In order to solve the management problem of employment information in College of information science and engineering of Shandong University of Science and Technology, we design and implement a data mining system for graduates' information. Through this system a great deal of tedious work can be automatically processed and orderly managed by computer. The system can ensure the data consistency and accuracy, provide employment knowlege dynamically, accurately and completely and ensure the quality of employment management. Also the system can provide digital, standardized and scientific management of employment information for colleges.(2) At present, with the rapid growth of the graduates' data base scale, traditional retrieval mechanism and statistical analysis method of database management system can't meet the real needs, and it becomes more and more urgent to mine useful information and knowledge from the database automatically, quickly and intelligently. Therefore, this thesis introduces association rules discovery algorithm and classification method based on decision tree. These algorithms are employed to discover relevant knowledge, which can be used to analyze the data and to find data attributes' influence on job-hunting aspects. The decision-making attributes' impact on employment categories can be used to guide us to improve the employment rate, raise employment levels and improve the existing mechanism. Furthermore, it can provide personalized guidance for students and reasonable predicts for the whole employment status.