Abstract
Many knowledge representation mechanisms consist of link-based structures; they may be studied formally by means of unordered trees. Here we consider the case where labels on the nodes are nonexistent or unreliable, and propose data mining processes focusing on just the link structure. We propose a representation of ordered trees, describe a combinatorial characterization and some properties, and use them to propose an efficient algorithm for mining frequent closed subtrees from a set of input trees. Then we focus on unordered trees, and show that intrinsic characterizations of our representation provide for a way of avoiding the repeated exploration of unordered trees, and then we give an efficient algorithm for mining frequent closed unordered trees.
Partially supported by the 6th Framework Program of EU through the integrated project DELIS (#001907), by the EU PASCAL Network of Excellence, IST-2002-506778, by the MEC TIN2005-08832-C03-03 (MOISES-BAR), MCYT TIN2004-07925-C03-02 (TRANGRAM), and CICYT TIN2004-04343 (iDEAS) projects.
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Balcázar, J.L., Bifet, A., Lozano, A. (2007). Mining Frequent Closed Unordered Trees Through Natural Representations . In: Priss, U., Polovina, S., Hill, R. (eds) Conceptual Structures: Knowledge Architectures for Smart Applications. ICCS 2007. Lecture Notes in Computer Science(), vol 4604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73681-3_26
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DOI: https://doi.org/10.1007/978-3-540-73681-3_26
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