Support vector machines based on K-means clustering for real-time business intelligence systems

Wang, J; Wu, X; Zhang, C

Support vector machines based on K-means clustering for real-time business intelligence systems

Wang, J Wu, X Zhang, C

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Publication Type:: Journal Article
Citation:: International Journal of Business Intelligence and Data Mining, 2005, 1 (1), pp. 54 - 64
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	International Journal of Business Intelligence and Data Mining, 2005, 1 (1), pp. 54 - 64	en_US
dc.identifier.issn	1743-8187	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4104
dc.description.abstract	Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response of SVM classifiers by reducing the number of support vectors. This is done by the K-means SVM (KMSVM) algorithm proposed in this paper. The KMSVM algorithm combines the K-means clustering technique with SVM and requires one more input parameter to be determined: the number of clusters. The criterion and strategy to determine the input parameters in the KMSVM algorithm are given in this paper. Experiments compare the KMSVM algorithm with SVM on real-world databases, and the results show that the KMSVM algorithm can speed up the response time of classifiers by both reducing support vectors and maintaining a similar testing accuracy to SVM. Copyright © 2005 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Business Intelligence and Data Mining	en_US
dc.relation.isbasedon	10.1504/IJBIDM.2005.007318	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Support vector machines based on K-means clustering for real-time business intelligence systems	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0804 Data Format	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/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	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response of SVM classifiers by reducing the number of support vectors. This is done by the K-means SVM (KMSVM) algorithm proposed in this paper. The KMSVM algorithm combines the K-means clustering technique with SVM and requires one more input parameter to be determined: the number of clusters. The criterion and strategy to determine the input parameters in the KMSVM algorithm are given in this paper. Experiments compare the KMSVM algorithm with SVM on real-world databases, and the results show that the KMSVM algorithm can speed up the response time of classifiers by both reducing support vectors and maintaining a similar testing accuracy to SVM. Copyright © 2005 Inderscience Enterprises Ltd.

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http://hdl.handle.net/10453/4104