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New methods for finding associations in large data sets: Generalizing the maximal information coefficient (MIC)
Ignac, Tomasz; Sakhanenko, N. A.; Skupin, Alexander et al.
2012In Proceedings of the Ninth International Workshop on Computational Systems Biology, p. 39-42
Peer reviewed
 

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Abstract :
[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.
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Identifiers :
UNILU:UL-ARTICLE-2012-1245
Author, co-author :
Ignac, Tomasz ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Sakhanenko, N. A.
Skupin, Alexander  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Galas, David J. 
Language :
English
Title :
New methods for finding associations in large data sets: Generalizing the maximal information coefficient (MIC)
Publication date :
2012
Event name :
9th International Workshop on Computational Systems Biology
Event date :
June 2012
Journal title :
Proceedings of the Ninth International Workshop on Computational Systems Biology
Pages :
39-42
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 18 January 2014

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