An Efficient Approach for Mining Frequent Item sets with Transaction Deletion Operation

Abstract

Deletion of transactions in databases is common in real-world applications. Developing an efficient and effective mining algorithm to maintain discovered information is thus quite important in data mining fields. A lot of algorithms have been proposed in recent years, and the best of them is the pre-large-tree-based algorithm. However, this algorithm only rebuilds the final pre-large tree every deleted transactions. After that, the FP-growth algorithm is applied for mining all frequent item sets. The pre-large-tree-based approach requires twice the computation time needed for a single procedure. In this paper, we present an incremental mining algorithm to solve above issues. An itemset tidset-tree structure will be used to maintain large and pre-large item sets. The proposed algorithm only processes deleted transactions for updating some nodes in this tree, and all frequent item sets are directly derived from the tree traversal process. Experimental results show that the proposed algorithm has good performance.