Mining RDF metadata for generalized association rules

Mining RDF metadata for generalized association rules

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...

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Main Authors: JIANG, Tao, TAN, Ah-hwee
格式: text
語言:English
出版: Institutional Knowledge at Singapore Management University 2006
主題:
Association Rule
Resource Description Framework
Terrorist Group
Resource Description Framework Data
Root Closure
Theory and Algorithms
在線閱讀:https://ink.library.smu.edu.sg/sis_research/6574
https://ink.library.smu.edu.sg/context/sis_research/article/7577/viewcontent/Jiang_Tan2006_Chapter_MiningRDFMetadataForGeneralize.pdf
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機構: Singapore Management University
語言: English
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總結: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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