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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Bibliographic Details
Main Authors: JIANG, Tao, TAN, Ah-hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
Subjects:
Association Rule
Resource Description Framework
Terrorist Group
Resource Description Framework Data
Root Closure
Theory and Algorithms
Online Access: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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Institution: Singapore Management University
Language: English
Description
Summary: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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