PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mining Implications from Lattices of Closed Trees
José Balcázar, Albert Bifet and Antoni Lozano
In: EGC'08: 8èmes journées francophones "Extraction et Gestion des Connaissances, 29 January -1 February, Sophia Antipolis, France.


We propose a way of extracting high-confidence association rules from datasets consisting of unlabeled trees. The antecedents are obtained through a computation akin to a hypergraph transversal, whereas the consequents follow from an application of the closure operators on unlabeled trees developed in previous recent works of the authors. We discuss in more detail the case of rules that always hold, independently of the dataset, since these are more complex than in itemsets due to the fact that we are no longer working on a lattice.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3226
Deposited By:Albert Bifet
Deposited On:21 January 2008