Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era

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 emphgeneralization closure for systematic...

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Main Authors:	JIANG, Tao, TAN, Ah-hwee
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2006
Subjects:	Association rule mining RDF mining Numerical Analysis and Scientific Computing Theory and Algorithms
Online Access:	https://ink.library.smu.edu.sg/sis_research/6696 https://ink.library.smu.edu.sg/context/sis_research/article/7699/viewcontent/1135777.1135960.pdf
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-7699
record_format	dspace
spelling	sg-smu-ink.sis_research-76992022-01-27T08:36:57Z Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era JIANG, Tao TAN, Ah-hwee 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 emphgeneralization closure for systematic over-generalization reduction. 2006-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6696 info:doi/10.1145/1135777.1135960 https://ink.library.smu.edu.sg/context/sis_research/article/7699/viewcontent/1135777.1135960.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Association rule mining RDF mining Numerical Analysis and Scientific Computing Theory and Algorithms
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Association rule mining RDF mining Numerical Analysis and Scientific Computing Theory and Algorithms
spellingShingle	Association rule mining RDF mining Numerical Analysis and Scientific Computing Theory and Algorithms JIANG, Tao TAN, Ah-hwee Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era
description	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 emphgeneralization closure for systematic over-generalization reduction.
format	text
author	JIANG, Tao TAN, Ah-hwee
author_facet	JIANG, Tao TAN, Ah-hwee
author_sort	JIANG, Tao
title	Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era
title_short	Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era
title_full	Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era
title_fullStr	Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era
title_full_unstemmed	Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era
title_sort	mining rdf metadata for generalized association rules: knowledge discovery in the semantic web era
publisher	Institutional Knowledge at Singapore Management University
publishDate	2006
url	https://ink.library.smu.edu.sg/sis_research/6696 https://ink.library.smu.edu.sg/context/sis_research/article/7699/viewcontent/1135777.1135960.pdf
_version_	1770576049258627072

Mining RDF metadata for generalized association rules: Knowledge discovery in the semantic web era

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