The Improvement Of Association Rules Mining Algorithm And Its Application In The Basic Skills Competition System Of Provincial Normal Students

With the rapid development of information technology and the explosion of global data,data mining came into being in the late 1980s.Data mining technology needs to integrate professional knowledge of many disciplines,including association rule mining.Apriori algorithm is a classical algorithm,which is widely used in many fields.However,it has some disadvantages.For example,it needs to scan the database many times and generates a large number of useless candidate itemsets.In this paper,we firstly make a deep analysis and research on data mining technology,especially association rule data mining technology.The classical Apriori algorithm is discussed in detail.Four improvement strategies of association rule algorithm in data mining are analyzed,based on which,we proposed two optimized algorithms for association rule mining.One is MApriori algorithm based on matrix and TID,the other is HApriori algorithm based on Hash and matrix.Both algorithms are based on matrix basis,since matrix operations can effectively reduce the execution time of a program.Both algorithms can reduce the number of generated frequent itemsets and transaction database scans.A comparison of the effects shows that they have been optimized to a better extent.Secondly,the association coefficient is introduced into the association rule algorithm,which effectively reduces the generation of redundant rules and brings great convenience for users to find the rules they need.Finally,applying the new association rule mining algorithm,a competition system in basic skills for normal university students is designed.After a preprocessing of the raw data of players,instead of using the traditional simple method of score range,the system inspects the position of the scores in the normal distribution to determine the grades of excellent,good,passing or failing,and then to mine the association rules related to the first prize.This provides help for normal universities to select recommended players,and also provides reference for government departments to formulate rules.In addition,the system has a player selection module.It inspects students' score reports collected from the educational administration system,and then makes a selection of players based on the association rules previously mined.Of course,with the help of the association rules mined by this system,students can predict follow-up courses,strengthen the study of weak subjects,and teachers can make targeted adjustments to teach students in accordance with their aptitude.Of course,if we add the growth data of graduated students after graduation into the system,such as all kinds of competition awards,scientific research growth data,management data,etc.,combining with their performance while in school,the system can mine the association rules related to personal growth after graduation.So.with the help of the rules,schools cap better formulate teaching programs and train more excellent educational and teaching talents.