Reference : New methods for finding associations in large data sets: Generalizing the maximal inf...
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/15092
New methods for finding associations in large data sets: Generalizing the maximal information coefficient (MIC)
English
Ignac, Tomasz mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Sakhanenko, N. A. [> >]
Skupin, Alexander mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Galas, David J. [> >]
2012
Proceedings of the Ninth International Workshop on Computational Systems Biology
39-42
Yes
9th International Workshop on Computational Systems Biology
June 2012
[en] We propose here a natural, but substantive, extension of the MIC. Defined for two variables, MIC has a distinct advance for detecting potentially complex dependencies. Our extension provides a similar means for dependencies among three variables. This itself is an important step for practical applications. We show that by merging two concepts, the interaction information, which is a generalization of the mutual information to three variables, and the normalized information distance, which measures informational sharing between two variables, we can extend the fundamental idea of MIC. Our results also exhibit some attractive properties that should be useful for practical applications in data analysis. Finally, the conceptual and mathematical framework presented here can be used to generalize the idea of MIC to the multi-variable case.
http://hdl.handle.net/10993/15092
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