Mining for combined association rules on multiple datasets

Zhao, Y; Zhang, H; Figueiredo, F; Cao, L; Zhang, C

Mining for combined association rules on multiple datasets

Zhao, Y Zhang, H Figueiredo, F Cao, L

Zhang, C

Permalink

Publication Type:: Conference Proceeding
Citation:: 2007 International Workshop on Domain Driven Data Mining, DDDM2007, 2007, pp. 18 - 23
Issue Date:: 2007-12-01

Closed Access

	Filename	Description	Size
	2006013586.pdf		766.44 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Figueiredo, F	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	2007 International Workshop on Domain Driven Data Mining, DDDM2007, 2007, pp. 18 - 23	en_US
dc.identifier.isbn	9781595938466	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6978
dc.description.abstract	Many organisations have their digital information stored in a distributed systems structure scheme, be it in different locations, using vertically and horizontally distributed repositories, which brings about an high level of complexity to data mining. From a classical data mining view, where the algorithms expect a denormalised structure to be able to operate on, heterogeneous data sources, such as static demographic and dynamic transactional data are to be manipulated and integrated to the extent commercial association rules algorithms can be applied. Bearing in mind the usefulness and understandability of the application from a business perspective, combined rules of multiple patterns derived from different repositories, containing historical and point in time data, were used to produce new techniques in association mining applied to debt recovery. Initially debt repayment patterns were discovered using transactional data and class labels defined by domain expertise, then demographic patterns were attached to each of the class labels. After combining the patterns, two type of rules were discovered leading to different results: 1) same demographic pattern with different repayment patterns, and 2) same repayment pattern with different demographic patterns. The rules produced are interesting, valuable, complete and understandable, which shows the applicability and effectiveness of the new method. Copyright 2007 ACM.	en_US
dc.relation.ispartof	2007 International Workshop on Domain Driven Data Mining, DDDM2007	en_US
dc.relation.isbasedon	10.1145/1288552.1288555	en_US
dc.title	Mining for combined association rules on multiple datasets	en_US
dc.type	Conference Proceeding
utslib.for	080610 Information Systems Organisation	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	San Jose, USA	en_US
dc.location.activity	ISI:000250133800005
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:

Many organisations have their digital information stored in a distributed systems structure scheme, be it in different locations, using vertically and horizontally distributed repositories, which brings about an high level of complexity to data mining. From a classical data mining view, where the algorithms expect a denormalised structure to be able to operate on, heterogeneous data sources, such as static demographic and dynamic transactional data are to be manipulated and integrated to the extent commercial association rules algorithms can be applied. Bearing in mind the usefulness and understandability of the application from a business perspective, combined rules of multiple patterns derived from different repositories, containing historical and point in time data, were used to produce new techniques in association mining applied to debt recovery. Initially debt repayment patterns were discovered using transactional data and class labels defined by domain expertise, then demographic patterns were attached to each of the class labels. After combining the patterns, two type of rules were discovered leading to different results: 1) same demographic pattern with different repayment patterns, and 2) same repayment pattern with different demographic patterns. The rules produced are interesting, valuable, complete and understandable, which shows the applicability and effectiveness of the new method. Copyright 2007 ACM.

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

http://hdl.handle.net/10453/6978