Abstract
Tree structures are used extensively in domains such as computational biology, pattern recognition, XML databases, computer networks, and so on. One important problem in mining databases of trees is to find frequently occurring subtrees. However, because of the combinatorial explosion, the number of frequent subtrees usually grows exponentially with the size of the subtrees. In this paper, we present CMTreeMiner, a computationally efficient algorithm that discovers all closed and maximal frequent subtrees in a database of rooted unordered trees. The algorithm mines both closed and maximal frequent subtrees by traversing an enumeration tree that systematically enumerates all subtrees, while using an enumeration DAG to prune the branches of the enumeration tree that do not correspond to closed or maximal frequent subtrees. The enumeration tree and the enumeration DAG are defined based on a canonical form for rooted unordered trees–the depth-first canonical form (DFCF). We compare the performance of our algorithm with that of PathJoin, a recently published algorithm that mines maximal frequent subtrees.
This work was supported by NSF under Grant Nos. 0086116, 0085773, and 9817773.
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Chi, Y., Yang, Y., Xia, Y., Muntz, R.R. (2004). CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees. 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_9
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DOI: https://doi.org/10.1007/978-3-540-24775-3_9
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