Research Article

Detecting Novel Associations in Large Data Sets

Science  16 Dec 2011:
Vol. 334, Issue 6062, pp. 1518-1524
DOI: 10.1126/science.1205438

You are currently viewing the abstract.

View Full Text

Abstract

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R2) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

View Full Text

Cited By...