Mining of weighted association rules based on algorithm Apriori

Association rules mining is mainly used to find frequent item sets in database.By taking weight value as a mark of the importance of individual item,mining with a new association rule—weighted association rule was proposed.Due to the introduction of this items weight,the truth of Apriori would not hold further.The subset of frequent item set would not also be exactly frequent.Thus,a concept of k-support minimum value of item sets was set forth,and an algorithm to discover weighted association rules was proposed.Using this approach,the items with low frequency and high profit could be mined,and the association rules were mined more to meet the needs of decision makers,and also more meet the practical needs.