Cooperative strategy for web data mining and cleaning

Li, Y; Zhang, C; Zhang, S

Cooperative strategy for web data mining and cleaning

Li, Y Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: Applied Artificial Intelligence, 2003, 17 (5-6), pp. 443 - 460
Issue Date:: 2003-05-01

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Field	Value	Language
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2003-05-01	en_US
dc.identifier.citation	Applied Artificial Intelligence, 2003, 17 (5-6), pp. 443 - 460	en_US
dc.identifier.issn	0883-9514	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4002
dc.description.abstract	While the Internet and World Wide Web have put a huge volume of low-quality information at the easy access of an information gathering system, filtering out irrelevant information has become a big challenge. In this paper, a Web data mining and cleaning strategy for information gathering is proposed. A data-mining model is presented for the data that come from multiple agents. Using the model, a data-cleaning algorithm is then presented to eliminate irrelevant data. To evaluate the data-cleaning strategy, an interpretation is given for the mining model according to evidence theory. An experiment is also conducted to evaluate the strategy using Web data. The experimental results have shown that the proposed strategy is efficient and promising. © 2003 Taylor and Francis Group, LLC.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation.ispartof	Applied Artificial Intelligence	en_US
dc.relation.isbasedon	10.1080/713827173	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Cooperative strategy for web data mining and cleaning	en_US
dc.type	Journal Article
utslib.citation.volume	5-6	en_US
utslib.citation.volume	17	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	5-6	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

While the Internet and World Wide Web have put a huge volume of low-quality information at the easy access of an information gathering system, filtering out irrelevant information has become a big challenge. In this paper, a Web data mining and cleaning strategy for information gathering is proposed. A data-mining model is presented for the data that come from multiple agents. Using the model, a data-cleaning algorithm is then presented to eliminate irrelevant data. To evaluate the data-cleaning strategy, an interpretation is given for the mining model according to evidence theory. An experiment is also conducted to evaluate the strategy using Web data. The experimental results have shown that the proposed strategy is efficient and promising. © 2003 Taylor and Francis Group, LLC.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/4002