On Improved 3MIC Algorithm on Exploring Large Data Sets with Multi-variables and Application

Applying for information entropy theory, we present a measure of dependence for three-variable relationships: the three variables maximal information coefficient (3MIC). It is a kind of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships in large data sets. 3MIC generalizes the Maximum information coefficient (MIC) measurement in mutual variables. The improved uniform grid is proposed by data grid idea. At the same time some optimal single axis partition algorithm (SAR) is built to ensure the feasible of the 3MIC measurement. Finally we apply the 3MIC to analysis the data sets of physical measurement among college students.