An integrated analysis method for bank customer classification

Zhang, J; Lu, J; Zhang, G; Yan, X

An integrated analysis method for bank customer classification

Zhang, J Lu, J

Zhang, G

Yan, X

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Publication Type:: Conference Proceeding
Citation:: Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006, 2006, pp. 247 - 252
Issue Date:: 2006-01-01

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Field	Value	Language
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Yan, X	en_US
dc.date.issued	2006-01-01	en_US
dc.identifier.citation	Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006, 2006, pp. 247 - 252	en_US
dc.identifier.isbn	9812566902	en_US
dc.identifier.isbn	9789812566904	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1946
dc.description.abstract	© 2006 by World Scientific Publishing Co. Pte. Ltd. Customer classification is one of the major tasks in customer relationship management. Customers have both static characteristics and dynamic behavioral features. To apply both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In this paper, customer dynamic data is clustered using a hybrid genetic algorithm and then is combined with customer static data to give reasonable customer segmentation by using neural network technique. A novel classification method which considers both the static and dynamic data of customers is proposed. Applying the proposed method in a bank’s datasets can obviously improve the accuracy of customer classification comparing with traditional methods where only static data is used.	en_US
dc.relation.ispartof	Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006	en_US
dc.relation.isbasedon	10.1142/9789812774118_0037	en_US
dc.title	An integrated analysis method for bank customer classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Genova, Italy	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2006 by World Scientific Publishing Co. Pte. Ltd. Customer classification is one of the major tasks in customer relationship management. Customers have both static characteristics and dynamic behavioral features. To apply both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In this paper, customer dynamic data is clustered using a hybrid genetic algorithm and then is combined with customer static data to give reasonable customer segmentation by using neural network technique. A novel classification method which considers both the static and dynamic data of customers is proposed. Applying the proposed method in a bank’s datasets can obviously improve the accuracy of customer classification comparing with traditional methods where only static data is used.

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

http://hdl.handle.net/10453/1946