Maintenance of prelarge trees for data mining with modified records

The frequent pattern tree (FP-tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It is used to compress a database into a tree structure which stores only large items. When data are modified, it, however, needs to process all transactions in a batch way. In the past, the prelarge-tree structure was proposed to incrementally mine association rules efficiently. In this paper, we propose an algorithm to maintain this structure when records in an original database are modified. The proposed maintenance algorithm is based on the pre-large concepts, which are defined by a lower support threshold and an upper support threshold. Due to the pruning properties of pre-large concepts, the proposed approach can reduce the rescan number of an original database when records are modified. It can thus obtain good execution performance for pre-large tree maintenance, especially when each time a small number of records are modified. Although experimental results show that the proposed prelarge-tree maintenance algorithm has good performance for handling modified records, the proposed algorithm needs to maintain nodes of pre-large items in the tree structure. This is the additional overhead, which is a trade-off between execution time and tree complexity.