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We formulate an info-clustering paradigm based on a multivariate informationmeasure, called multivariate mutual information, that naturally extendsShannon's mutual information between two random variables to the multivariatecase involving more than two random variables. With proper model reductions, weshow that the paradigm can be applied to study the human genome and connectomein a more meaningful way than the conventional algorithmic approach. Not onlycan info-clustering provide justifications and refinements to some existingtechniques, but it also inspires new computationally feasible solutions.

Info-Clustering: A Mathematical Theory for Data Clustering