Two novel interestingness measures for gene association rule mining

Abstract

Recent research has shown that association rules are useful in gene expression data analysis. Interestingness measure plays an important role in the association rule mining on small sample size, high dimensionality, and noisy gene expression data. This work introduces two interestingness measures by exploring prior knowledge contained in open biological databases. They are Max-Pathway-Distance (MaxPD), which explores the gene’s relativity in Kyoto encyclopedia of genes and genomes Pathway, and Max-Chromosomal-Distance (MaxCD), which makes use of the distance among genes in the chromosome. The properties of our proposed interestingness measures are also explored to mine the interesting rules efficiently. Experimental results on four real-life gene expression datasets show the effectiveness of MaxPD and MaxCD in both classification accuracy and biological interpretability.