Abstract
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.
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Jiang, T., Tan, AH. (2006). Mining RDF Metadata for Generalized Association Rules. In: Bressan, S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2006. Lecture Notes in Computer Science, vol 4080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11827405_22
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DOI: https://doi.org/10.1007/11827405_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37871-6
Online ISBN: 978-3-540-37872-3
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