Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New Method for Finding Generalized Frequent Itemsets in Generalized Association Rule Mining
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
Mining Generalized Substructures from a Set of Labeled Graphs
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
A distributed recommender system architecture
International Journal of Web Engineering and Technology
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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.