GM-Tree: An efficient frequent pattern mining technique for dynamic database

Since its inception, mining frequent patterns have become an imperative issue in data mining. The main problem in this area is to find out the association rule that identifies the relationships among a set of items. But the most expensive step in association rule is finding frequent itemsets and hence it draw the attention of many important research. In this paper, we propose a novel tree structure, called GM(Generate and Merge)Tree, which is a combination of prefix based incremental mining using canonical ordering and batch incrementing techniques. Our approach makes the tree structure more compact, canonically ordered of nodes and avoids sequential incrementing of transactions. It also helps to give a scalable algorithm with minimum overheads of modifying the tree structure during update operations. This algorithm is especially expected to give better results in case of extremely large transaction database in a dynamic environment. The experimental work has been carried out on two large datasets. Test results show the efficiency and effectiveness of the proposed approach by outperforming the traditional FP-Tree, CanTree(Canonical-order Tree) and BIT(Batch Incremental Tree).