An artificial intelligence driven multi-feature extraction scheme for big data detection

Wan, J; Zheng, P; Si, H; Xiong, NN; Zhang, W; Vasilakos, AV

An artificial intelligence driven multi-feature extraction scheme for big data detection

Wan, J Zheng, P Si, H Xiong, NN Zhang, W Vasilakos, AV

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 80122 - 80132
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Wan, J	en_US
dc.contributor.author	Zheng, P	en_US
dc.contributor.author	Si, H	en_US
dc.contributor.author	Xiong, NN	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Vasilakos, AV https://orcid.org/0000-0003-1902-9877	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 80122 - 80132	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137502
dc.description.abstract	© 2019 IEEE. The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2923583	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An artificial intelligence driven multi-feature extraction scheme for big data detection	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2019 IEEE. The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well.

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http://hdl.handle.net/10453/137502