Efficient pattern-growth methods for frequent tree pattern mining

Abstract

Mining frequent tree patterns is an important research problems with broad applications in bioinformatics, digital library, ecommerce, and so on. Previous studies highly suggested that patterngrowth methods are efficient in frequent pattern mining. In this paper, we systematically develop the pattern growth methods for mining frequent tree patterns. Two algorithms, Chopper and XSpanner, are devised. An extensive performance study shows that the two newly developed algorithms outperform TreeMinerV [13], one of the fastest methods proposed before, in mining large databases. Furthermore, algorithm XSpanner is substantially faster than Chopper in many cases.