The breakthrough method from Reshef brothers (described in a recent Science paper) improves upon Pearson correlation coefficient and introduces a new MIC criteria to find a wide range of non-linear association. The corresponding software is available in Java and R.
The maximal information coefficient (MIC) is a new and very promising measure of two-variable dependence designed specifically for rapid exploration of many-dimensional data sets.
MIC is a part of a larger family of maximal information-based nonparametric exploration (MINE) statistics, which can be used to identify and characterize important relationships in data.
Paper: Detecting Novel Associations in Large Data Sets, David N. Reshef, et al., Science 334, 1518 (2011); DOI: 10.1126/science.1205438
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
See also a very good video which explains MIC visualization of datasets