The Research And Application Of Data Mining Technology In Decision-Assisting Analysis System Of Graduate Employment In Liaoning Province

Because of the presence of large amounts of data in database, how to discover the useful information from database has become very important and data mining technology is used to solve this problem. As far as data mining technology is concerned, the research both at home and abroad has achieved many remarkable achievements, which have successfully applied to many fields, but the application in education is not widespread.Due to the rapid popularization of higher education in China, the expansion of enrollment in the institutions of higher learning and the explosion in the number of college students, employment issues have become increasingly prominent. Although governments at all levels have released a variety of positive policies to promote employment, colleges and universities also have tried their best to improve the employment rate, the situation of employment remains severe. The employment problem can not be solved in a short period of time because the supply of college graduates is greater than the total demand of social development. Besides, the unreasonable structure and scale of disciplines in higher education have attracted increasing attention. Therefore, it is urgent to find the principle to adjust the the structure and scale of disciplines in higher education, to improve the basic conditions for running schools and to reduce the impact of the structural contradictions of disciplines on college students employment.The object of study is the employment data of college graduates in Liaoning Province since 2003. The author applies data mining technology to the study and fully realizes the classification of data mining process, which includes: identifying targets and goals of data mining, data acquisition, data integration, data cleaning up, data conversion, and such kind of data pre-processing technology. Because the classification of employment data has the characteristics of predictability and divergency, the author chooses decision tree C4.5 algorithm to generate decision-making classification tree, which can finally generate classification rules. And the rules of college students employment can provide policy-making basis and reduce policy-making risk for adjusting the structure and scale of employment-oriented disciplines in higher education.