An effective contrast sequential pattern mining approach to taxpayer behavior analysis

Zheng, Z; Wei, W; Liu, C; Cao, W; Cao, L; Bhatia, M

An effective contrast sequential pattern mining approach to taxpayer behavior analysis

Zheng, Z Wei, W Liu, C Cao, W Cao, L

Bhatia, M

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Publication Type:: Journal Article
Citation:: World Wide Web, 2016, 19 (4), pp. 633 - 651
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Zheng, Z	en_US
dc.contributor.author	Wei, W	en_US
dc.contributor.author	Liu, C	en_US
dc.contributor.author	Cao, W	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Bhatia, M	en_US
dc.date.issued	2016-07-01	en_US
dc.identifier.citation	World Wide Web, 2016, 19 (4), pp. 633 - 651	en_US
dc.identifier.issn	1386-145X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122644
dc.description.abstract	© 2015, Springer Science+Business Media New York. Data mining for client behavior analysis has become increasingly important in business, however further analysis on transactions and sequential behaviors would be of even greater value, especially in the financial service industry, such as banking and insurance, government and so on. In a real-world business application of taxation debt collection, in order to understand the internal relationship between taxpayers’ sequential behaviors (payment, lodgment and actions) and compliance to their debt, we need to find the contrast sequential behavior patterns between compliant and non-compliant taxpayers. Contrast Patterns (CP) are defined as the itemsets showing the difference/discrimination between two classes/datasets (Dong and Li, 1999). However, the existing CP mining methods which can only mine itemset patterns, are not suitable for mining sequential patterns, such as time-ordered transactions in taxpayer sequential behaviors. Little work has been conducted on Contrast Sequential Pattern (CSP) mining so far. Therefore, to address this issue, we develop a CSP mining approach, eCSP, by using an effective CSP-tree structure, which improves the PrefixSpan tree (Pei et al., 2001) for mining contrast patterns. We propose some heuristics and interestingness filtering criteria, and integrate them into the CSP-tree seamlessly to reduce the search space and to find business-interesting patterns as well. The performance of the proposed approach is evaluated on three real-world datasets. In addition, we use a case study to show how to implement the approach to analyse taxpayer behaviour. The results show a very promising performance and convincing business value.	en_US
dc.relation.ispartof	World Wide Web	en_US
dc.relation.isbasedon	10.1007/s11280-015-0350-4	en_US
dc.subject.classification	Information Systems	en_US
dc.title	An effective contrast sequential pattern mining approach to taxpayer behavior analysis	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	19	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	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/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
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
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2015, Springer Science+Business Media New York. Data mining for client behavior analysis has become increasingly important in business, however further analysis on transactions and sequential behaviors would be of even greater value, especially in the financial service industry, such as banking and insurance, government and so on. In a real-world business application of taxation debt collection, in order to understand the internal relationship between taxpayers’ sequential behaviors (payment, lodgment and actions) and compliance to their debt, we need to find the contrast sequential behavior patterns between compliant and non-compliant taxpayers. Contrast Patterns (CP) are defined as the itemsets showing the difference/discrimination between two classes/datasets (Dong and Li, 1999). However, the existing CP mining methods which can only mine itemset patterns, are not suitable for mining sequential patterns, such as time-ordered transactions in taxpayer sequential behaviors. Little work has been conducted on Contrast Sequential Pattern (CSP) mining so far. Therefore, to address this issue, we develop a CSP mining approach, eCSP, by using an effective CSP-tree structure, which improves the PrefixSpan tree (Pei et al., 2001) for mining contrast patterns. We propose some heuristics and interestingness filtering criteria, and integrate them into the CSP-tree seamlessly to reduce the search space and to find business-interesting patterns as well. The performance of the proposed approach is evaluated on three real-world datasets. In addition, we use a case study to show how to implement the approach to analyse taxpayer behaviour. The results show a very promising performance and convincing business value.

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http://hdl.handle.net/10453/122644