Coupled nominal similarity in unsupervised learning

Wang, C; Cao, L; Wang, M; Li, J; Wei, W; Ou, Y

Coupled nominal similarity in unsupervised learning

Wang, C Cao, L

Wang, M Li, J Wei, W Ou, Y

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 973 - 978
Issue Date:: 2011-12-13

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Field	Value	Language
dc.contributor.author	Wang, C	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Wang, M	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Wei, W	en_US
dc.contributor.author	Ou, Y	en_US
dc.date.issued	2011-12-13	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 973 - 978	en_US
dc.identifier.isbn	9781450307178	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30830
dc.description.abstract	The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement. © 2011 ACM.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/2063576.2063715	en_US
dc.title	Coupled nominal similarity in unsupervised learning	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Glasgow, UK
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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement. © 2011 ACM.

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http://hdl.handle.net/10453/30830