On a possibility of applying interrelationship mining to gene expression data analysis

Abstract

Interrelationship mining was proposed by the authors to extract characteristics of objects based on interrelationships between attributes. Interrelationship mining is an extension of rough set-based data mining, which enables us to extract characteristics based on comparison of values of two different attributes such that "the value of attribute a is higher than the value of attribute b." In this paper, we discuss an approach of applying the interrelationship mining to bioinformatics, in particular, gene expression data analysis. © Springer International Publishing 2013.