Data mining applications in social security

Zhao, Y; Zhang, H; Cao, L; Bohlscheid, H; Ou, Y; Zhang, C

Data mining applications in social security

Zhao, Y Zhang, H Cao, L

Bohlscheid, H Ou, Y Zhang, C

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Publication Type:: Chapter
Citation:: Data Mining for Business Applications, 2009, pp. 81 - 96
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Bohlscheid, H	en_US
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Data Mining for Business Applications, 2009, pp. 81 - 96	en_US
dc.identifier.isbn	9780387794198	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7859
dc.description.abstract	This chapter presents four applications of data mining in social security. The first is an application of decision tree and association rules to find the demographic patterns of customers. Sequence mining is used in the second application to find activity sequence patterns related to debt occurrence. In the third application, combined association rules are mined from heterogeneous data sources to discover patterns of slow payers and quick payers. In the last application, clustering and analysis of variance are employed to check the effectiveness of a new policy. © 2009 Springer US.	en_US
dc.relation.ispartof	Data Mining for Business Applications	en_US
dc.relation.isbasedon	10.1007/978-0-387-79420-4_6	en_US
dc.title	Data mining applications in social security	en_US
dc.type	Chapter
utslib.for	080610 Information Systems Organisation	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
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.publication-status	Published	en_US

Abstract:

This chapter presents four applications of data mining in social security. The first is an application of decision tree and association rules to find the demographic patterns of customers. Sequence mining is used in the second application to find activity sequence patterns related to debt occurrence. In the third application, combined association rules are mined from heterogeneous data sources to discover patterns of slow payers and quick payers. In the last application, clustering and analysis of variance are employed to check the effectiveness of a new policy. © 2009 Springer US.

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

http://hdl.handle.net/10453/7859