Efficiently mining frequent trees in a forest: algorithms and applications
Zaki, M.J.
Knowledge and Data Engineering, IEEE Transactions on
Volume 17, Issue 8, Aug. 2005 Page(s): 1021 - 1035
Digital Object Identifier   10.1109/TKDE.2005.125
Summary: Mining frequent trees is very useful in domains like bioinformatics, Web mining, mining semistructured data, etc. We formulate the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. We present TREEMINER, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scope-list. We contrast TREEMINER with a pattern matching tree mining algorithm (PATTERNMATCHER), and we also compare it with TREEMINERD, which counts only distinct occurrences of a pattern. We conduct detailed experiments to test the performance and scalability of these methods. We also use tree mining to analyze RNA structure and phylogenetics data sets from bioinformatics domain.

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