A New Structural Approach for Mining Frequent Itemset

The problem of mining high quality frequent substructures from a large collection of semi-structured data has recently attracted a lot of research. There are various efficient algorithms available for discovering frequent substructures in a large structured data, where both of the patterns and the data are modeled by labeled unordered trees. In this paper, a new structure based algorithm ENHANCE CLIQUE TREE is proposed which creates the unordered structure of itemsets and gives the perceptive representation of the mining results. It eliminates the limitation of FP-tree which always follows the ordered tree structure and its performance also degrades when the dataset is regularly updated. Here a VERIFY TREE ALGORITHM is added in the proposed algorithm which verifies that whether there is a need to restructure the tree or not when the itemsets are inserted or deleted. It improves the performance so that it will reduce unnecessary space and time used for restructure the tree when there is no need for it.