The maintenance of representative frequent itemsets

Mining frequent itemsets is to discover the groups of items appearing always together excess of a user specified threshold from a transaction database. However, there may be many frequent itemsets existing in a transaction database, such that it is difficult to make a decision for a decision maker. Recently, mining frequent closed itemsets becomes a major research issue, since all frequent itemsets can be derived from frequent closed itemsets. In addition, the transactions in a database will be increased and removed constantly. It is a challenge that how to update the previous frequent closed itemsets from the increased and removed transactions. In our previous researches, we have proposed an algorithm MRFI to maintain the frequent closed itemsets when the transactions are added into a transaction database. In this paper, we propose an efficient algorithm for maintaining frequent closed itemsets when the transactions are deleted from a transaction database without scanning original database. Our algorithm updates closed itemsets by some rules without taking a lot of time to search the previous closed itemsets. The experimental results show that our algorithm significantly outperforms the previous approaches which need to take a lot of time to search the previous closed itemsets.