Applied Research On Data Mining In The Financial Analysis Of University

Currently,with the rapid development of colleges and universities education and the deepening of reforms,the gradual change of financial management environment in institutions of higher education and financial management's functions,higher requirements have been put forward for the financial analysis of colleges and universities.However,in financial analysis of colleges and universities,both in theory and in practice studies,there are some limitations. Although computer technology and network technology has been widely used in the financial management of colleges and universities,this is limited to the financial accounting and financial analysis is less involved.A large number of financial data's main purpose is to provide simple queries and statistical reports. The in-depth information hidden in the data has not been fully used.At this time the combination of data mining methods and financial analysis methods can receive better results.In this thesis,the main problem is the risk analysis of students' payment in the colleges and universities financial management.Through the research of data mining technology,with the integration of the characteristics of financial management system of colleges and universities,the use of data mining theory and methods in financial analysis of colleges and universities is put forward,and good results are received.The main contents of the study includes:for the financial management features,the introduction of risk-weight to improve the ID3 algorithm to enable the ID3 decision tree algorithm to classify better based on the risk-weight of various elements of financial data;the combination of the increased learning and decision-making Tree structure Algorithm to enable decision tree algorithm to handle the increased data;for current college students' owe,this practical problems,combined with data warehousing,data mining and other related technology,the design of a set of an early warning system program for college students' owe to monitor the current state of payment dynamically and accurately and predict high-risk owing students to provide the more scientific basis for the management decision of the leadership of colleges and universities.