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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: JIANG, Tao, TAN, Ah-hwee
التنسيق: text
اللغة:English
منشور في: Institutional Knowledge at Singapore Management University 2006
الموضوعات:
الوصول للمادة أونلاين: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
الوسوم: إضافة وسم
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المؤسسة: Singapore Management University
اللغة: English
الوصف
الملخص: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.