Difference detection between two contrast sets

Huang, HJ; Qin, Y; Zhu, X; Zhang, J; Zhang, S

Difference detection between two contrast sets

Huang, HJ Qin, Y Zhu, X Zhang, J Zhang, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4081 LNCS pp. 481 - 490
Issue Date:: 2006-01-01

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Field	Value	Language
dc.contributor.author	Huang, HJ	en_US
dc.contributor.author	Qin, Y	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2006-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4081 LNCS pp. 481 - 490	en_US
dc.identifier.isbn	3540377360	en_US
dc.identifier.isbn	9783540377368	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1991
dc.description.abstract	Mining group differences is useful in many applications, such as medical research, social network analysis and link discovery. The differences between groups can be measured from either statistical or data mining perspective. In this paper, we propose an empirical likelihood (EL) based strategy of building confidence intervals for the mean and distribution differences between two contrasting groups. In our approach we take into account the structure (semi-parametric) of groups, and experimentally evaluate the proposed approach using both simulated and real-world data. The results demonstrate that our approach is effective in building confidence intervals for group differences such as mean and distribution function. © Springer-Verlag Berlin Heidelberg 2006.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Difference detection between two contrast sets	en_US
dc.type	Conference Proceeding
utslib.citation.volume	4081 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Poland	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	4081 LNCS	en_US

Abstract:

Mining group differences is useful in many applications, such as medical research, social network analysis and link discovery. The differences between groups can be measured from either statistical or data mining perspective. In this paper, we propose an empirical likelihood (EL) based strategy of building confidence intervals for the mean and distribution differences between two contrasting groups. In our approach we take into account the structure (semi-parametric) of groups, and experimentally evaluate the proposed approach using both simulated and real-world data. The results demonstrate that our approach is effective in building confidence intervals for group differences such as mean and distribution function. © Springer-Verlag Berlin Heidelberg 2006.

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

http://hdl.handle.net/10453/1991