Abstract
Mining frequent tree patterns is an important research problems with broad applications in bioinformatics, digital library, e-commerce, and so on. Previous studies highly suggested that pattern-growth 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 TreeMinerVÂ [13], one of the fastest methods proposed before, in mining large databases. Furthermore, algorithm XSpanner is substantially faster than Chopper in many cases.
This research is supported in part by the Key Program of National Natural Science Foundation of China (No. 69933010), China National 863 High-Tech Projects (No. 2002AA4Z3430 and 2002AA231041), and US NSF grant IIS-0308001.
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Wang, C., Hong, M., Pei, J., Zhou, H., Wang, W., Shi, B. (2004). Efficient Pattern-Growth Methods for Frequent Tree Pattern Mining. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_54
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DOI: https://doi.org/10.1007/978-3-540-24775-3_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
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