In this paper, we present a novel frequent generalized pattern mining algorithm, called GP-Close, for mining generalized associations from RDF metadata. To solve the over-generalization problem encountered by existing methods, GP-Close employs the notion of generalization closure for systematic over-generalization reduction. Empirical experiments conducted on real world RDF data sets show that our method can substantially reduce pattern redundancy and perform much better than the original generalized association rule mining algorithm Cumulate in term of time efficiency. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Jiang, T., & Tan, A. H. (2006). Mining RDF metadata for generalized association rules. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4080 LNCS, pp. 223–233). Springer Verlag. https://doi.org/10.1007/11827405_22
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