An Efficient Approach for Incrementally Mining Frequent Closed Itemsets

Mining frequent item sets 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 item sets existing in a transaction database, such that it is difficult to make a decision for a decision maker. Recently, mining frequent closed item sets becomes a major research issue, since a set of the frequent closed item sets is a condensed and complete representation of the frequentitemsets and all the frequent item sets can be derived from the frequent closed item sets. However, the transactions in a transaction database will grow rapidly in a short time, and the frequent closed item sets may be changed due to the addition of the new transactions. This paper proposes an efficient algorithm for incrementally mining frequent closed item sets without scanning the original database and searching the previous closed item sets. The experimental results also show that our algorithm significantly outperforms the previous approaches which need to take a lot of time to search the previous closed item sets.