Abstract: A method to identify a relationship between at least pairs of variables in a dataset generates a novel measure of dependence, referred to as a Maximal Information Coefficient (MIC), that captures a wide range of interesting associations (not limited to specific function types such as linear, exponential or periodic, or to all functional relationships), and that gives similar scores to equally noisy relationships of different types. MIC is based on the idea that if a relationship exists between two variables, then a grid can be drawn on a scatterplot of the two variables that partitions the data to encapsulate that relationship.

Abstract: A method to identify a relationship between at least pairs of variables in a dataset generates a novel measure of dependence, referred to as a Maximal Information Coefficient (MIC), that captures a wide range of interesting associations (not limited to specific function types such as linear, exponential or periodic, or to all functional relationships), and that gives similar scores to equally noisy relationships of different types. MIC is based on the idea that if a relationship exists between two variables, then a grid can be drawn on a scatterplot of the two variables that partitions the data to encapsulate that relationship.