Because of its important application value in almost every region, early-warning has received extensive concern. This paper puts forward a study early-warning mechanism based on association rules. It uses an Apriori mining algorithm with some corresponding restrictions to dig out the latent school record association rules from former students' scores which are viewed as a history resource. Then these rules will be used to match up the data sets that need to monitor. Once a record is matched to one of these rules, the student of this record will receive an early-warning. This kind of early-warning mechanism changes the situation that problems with study can only be detected after knowing the scores. It has a predictable and forward-looking capability and has been proved to obtain a good early-warning effect during actual verification.
The early warning is beneficial to prevent and solve hidden trouble in good time,accordingly warning study has important value.The mechanism and method of the early warning are concerned widely.A new warning mechanism was proposed to construct early warning system based on domain early warning knowledge in the paper.Concentrated mainly in mining early warning rules based on background domain knowledge from the real data,to make early warning knowledge richer,more complete and with more practical value.Applying the mechanism and method to resolve early warning problems of student achievement data in a university educational administration management information system.Experimental results suggest that they can work reasonably well.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.