A novel method for mining frequent subtrees from XML data

In this paper, we focus on the problem of finding frequent subtrees in a large collection of XML data, where both of the patterns and the data are modeled by labeled ordered trees. We present an efficient algorithm RSTMiner that computes all rooted subtrees appearing in a collection of XML data trees with frequent above a user-specified threshold using a special structure Me-tree. In this algorithm, Me-tree is used as a merging tree to supply scheme information for efficient pruning and mining frequent sub-trees. The keys of the algorithm are efficient pruning candidates with Me-Tree structure and incrementally enumerating all rooted sub-trees in canonical form based on a extended right most expansion technique. Experiment results show that RSTMiner algorithm is efficient and scalable.