PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mining Frequent Closed Unordered Trees Through Natural Representations
José Balcázar, Albert Bifet and Antoni Lozano
In: 15th International Conference on Conceptual Structures (ICCS 2007), 22-27 July 2007, Sheffield, UK.

Abstract

Many knowledge representation mechanisms consist of linkbased 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3223
Deposited By:Albert Bifet
Deposited On:21 January 2008