Approximate processing method and system for maximum information coefficient between single variable and multiple variables

Abstract

The invention belongs to the technical field of data mining, and discloses a method and a system for calculating a maximum information coefficient between a single variable and multiple variables in abig data set, which are used for calculating the maximum information coefficient between the single variable Y and m variables (X1, X2,..., Xm). The method: searching optimal grid division of a multi-dimensional (m + 1) space; , fixing division of m variables (X1, X2,..., Xm) by utilizing a maximum information coefficient algorithm of a single variable and m-1 variables; dividing a variable Y, and finding out more suitable division for the variable Y and m variables (X1, X2,..., Xm); and calculating a normalized maximum mutual information value to serve as a maximum information coefficient value. The problem that the existing method for calculating the maximum information coefficient cannot be applied to the calculation of the maximum information coefficient between a single variable anda multivariable can be solved; according to the approximate processing method and system for the maximum information coefficient between the single variable and the multiple variables, the maximum information coefficient value between the single variable and the multiple variables can be calculated.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to an approximate processing method and system for the maximum information coefficient between a single variable and multiple variables. Background technique [0002] The universal connection of things makes mining the correlation between variables from data a basic work with a wide range of applications, such as the correlation between genes and genes, the correlation between genes and cancer, and the correlation between population growth and birth rate The relationship between sex, diet structure and birth rate, and the correlation between diet structure and cancer incidence jointly affecting population growth, etc. [0003] At present, the statistics used to measure the degree of correlation between variables mainly include the following types. Pearson Correlation, which measures the matching degree of two variables on a line, can only detect the statistical line...

AI Technical Summary

Problems solved by technology

[0016] The existing technology that can calculate the maximum information coefficient cannot calculate the maximum information coefficient between a single variable and multiple variables, but in the actual large data set, the correlation between single variable and multiple variables exists widely

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