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http://dx.doi.org/10.5391/JKIIS.2011.21.5.569

Mining of Subspace Contrasting Sample Groups in Microarray Data  

Lee, Kyung-Mi (충북대학교 컴퓨터과학과, PT-ERC)
Lee, Keon-Myung (충북대학교 컴퓨터과학과, PT-ERC)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.5, 2011 , pp. 569-574 More about this Journal
Abstract
In this paper, we introduce the subspace contrasting group identification problem and propose an algorithm to solve the problem. In order to identify contrasting groups, the algorithm first determines two groups of which attribute values are in one of the contrasting ranges specified by the analyst, and searches for the contrasting groups while increasing the dimension of subspaces with an association rule mining strategy. Because the dimension of microarray data is likely to be tens of thousands, it is burdensome to find all contrasting groups over all possible subspaces by query generation. It is very useful in the sense that the proposed method allows to find those contrasting groups without analyst's involvement.
Keywords
subspace contrasting group; incremental data mining; microarray; data analysis; clustering;
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Times Cited By KSCI : 1  (Citation Analysis)
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