Negative correlations in collaboration: Concepts and algorithms

Li, J; Liu, Q; Zeng, T

Negative correlations in collaboration: Concepts and algorithms

Li, J

Liu, Q Zeng, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 463 - 471
Issue Date:: 2010-09-07

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Field	Value	Language
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.contributor.author	Liu, Q	en_US
dc.contributor.author	Zeng, T	en_US
dc.date.issued	2010-09-07	en_US
dc.identifier.citation	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 463 - 471	en_US
dc.identifier.isbn	9781450300551	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30915
dc.description.abstract	This paper studies efficient mining of negative correlations that pace in collaboration. A collaborating negative correlation is a negative correlation between two sets of variables rather than traditionally between a pair of variables. It signifies a synchronized value rise or fall of all variables within one set whenever all variables in the other set go jointly at the opposite trend. The time complexity is exponential in mining. The high efficiency of our algorithm is attributed to two factors: (i) the transformation of the original data into a bipartite graph database, and (ii) the mining of transpose closures from a wide transactional database. Applying to a Yeast gene expression data, we evaluate, by using Pearson's correlation coefficient and P-value, the biological relevance of collaborating negative correlations as an example among many real-life domains. © 2010 ACM.	en_US
dc.relation.ispartof	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining	en_US
dc.relation.isbasedon	10.1145/1835804.1835864	en_US
dc.title	Negative correlations in collaboration: Concepts and algorithms	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Washington DC
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
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper studies efficient mining of negative correlations that pace in collaboration. A collaborating negative correlation is a negative correlation between two sets of variables rather than traditionally between a pair of variables. It signifies a synchronized value rise or fall of all variables within one set whenever all variables in the other set go jointly at the opposite trend. The time complexity is exponential in mining. The high efficiency of our algorithm is attributed to two factors: (i) the transformation of the original data into a bipartite graph database, and (ii) the mining of transpose closures from a wide transactional database. Applying to a Yeast gene expression data, we evaluate, by using Pearson's correlation coefficient and P-value, the biological relevance of collaborating negative correlations as an example among many real-life domains. © 2010 ACM.

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http://hdl.handle.net/10453/30915