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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Mining RDF Metadata for Generalized Association Rules
verfasst von : Tao Jiang, Ah-Hwee Tan
Erschienen in: Database and Expert Systems Applications
Verlag: Springer Berlin Heidelberg
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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.