Abstract: Due to the periodicity of teaching and the change of teaching environment,the data of educational administration in colleges and universities have the characteristics of time series,and there are many association redundancy,so it is difficult to find out the efficient and interesting association rules.Although the sequential pattern mining algorithm can mine the time series frequent itemsets,it can not eliminate the association redundancy in educational administration data,and the utility and novelty of mining results can not meet the requirements.Therefore,this paper proposed a FUI_DK association rule mining algorithm based on association redundancy in the educational field.FUI_DK algorithm generates frequent candidate itemsets based on sequential pattern mining algorithm,and increases utility and interest to obtain high utility interesting itemsets based on the support,confidence of classical association rule algorithms,and the association rules satisfying the conditions are sorted out according to their support,confidence and utility.Finally,the result of association rules with high utility and high interest is obtained.The experiment contrast and mining result analysis are carried out on the data of a university student educational administration.The experimental results show that the FUI_DK algorithm has better time performance in the data mining of university educational administration,and the elimination rate of known association rules in the field can reach 43%,which can help colleges and universities to carry out time-saving and effective educational data mining.

Key words: Association rules, Domain knowledge, Educational administration data, High utility and interesting itemsets, Sequential pattern mining