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Mining frequent tree patterns has many applications in different areas suchas XML data, bioinformatics and World Wide Web. The crucial step in frequentpattern mining is frequency counting, which involves a matching operator tofind occurrences (instances) of a tree pattern in a given collection of trees.A widely used matching operator for tree-structured data is subtreehomeomorphism, where an edge in the tree pattern is mapped onto anancestor-descendant relationship in the given tree. Tree patterns that arefrequent under subtree homeomorphism are usually called embedded patterns. Inthis paper, we present an efficient algorithm for subtree homeomorphism withapplication to frequent pattern mining. We propose a compact data-structure,called occ, which stores only information about the rightmost paths ofoccurrences and hence can encode and represent several occurrences of a treepattern. We then define efficient join operations on the occ data-structure,which help us count occurrences of tree patterns according to occurrences oftheir proper subtrees. Based on the proposed subtree homeomorphism method, wedevelop an effective pattern mining algorithm, called TPMiner. We evaluate theefficiency of TPMiner on several real-world and synthetic datasets. Ourextensive experiments confirm that TPMiner always outperforms well-knownexisting algorithms, and in several cases the improvement with respect toexisting algorithms is significant.

Mining Rooted Ordered Trees under Subtree Homeomorphism