Research On Employment Destination Factors Based On Multi-valued Association Rule Mining Algorithm

With the continued enrollment of colleges and universities, graduate employmentbecomes an increasingly serious problem, causing widespread concern in the government,society, schools, families, and individuals. Following the great success in business inrecent years, in the field of education, data mining has drawn continuous attention in thefield of education. Using data mining techniques to research the employment situation hasgreat significance.In this thesis, a multi-valued association rule mining algorithm and multi-valuedattribute discretization ways were introduced. Weka platform java source codes and apriorialgorithm implementation process in weka were analyzed. Then thesis researched how totake advantage of the existing weka file to realize data discretization, also integratedconstrained association rules algorithm into weka to realize rules controllability.According to the theory of employment, thesis selected indicators from schools andeconomic dimensions and extracted data from national college basic state database systemand the CCER financial database. From different view in employment destinations,including governmental department, enterprise, study, flexible employment and localproject employment, paper analyzed tendency of students’ options in different regions,different types of schools and different school of teaching qualities.Through this empirical analysis, which using multi-valued association rule mining toanalyze the employment problem, we drew the employment destination is related witharea, school type and school education, but not directly related with the level of regionaleconomic development, student activities and the number of cooperating agencies signedby school. Meanwhile, multi-valued association rules were proved to be effective andrationality when it was used to analyze the influencing factors. The Open-source platform,weka, was adopted, also paved the way for system integration, from data acquisition,storage, to data background processing, mining and even explain.