Mining of association rules is one of the most important fields in data mining.In this paper

a new general incremental algorithm IAR for mining association rules is presented.The disting uishing feature of IAR is as follows:First

the selective scan strategy is adopted

which is based on the upper bound on the number of candidate itemsets;Second

IAR can efficiently update the discovered rules when the value of support threshold is changed

new transactions are added to the database

and obsolete ones are removed from it.Furthermore

based on the Kruskal-Katona theorem and the itemset equivalence class theory

we devise the technique for calculating the maximal number of candidate itemsets.Experiments show the prominent performance of the algorithm IAR.