A Selective Analysis of Microarray Data Using Association Rule Mining

Association analysis plays the vital role in the computational biology. DNA Microarrays allow for the simultaneously monitor of expression levels for thousands of genes or entire genomes. Microarray gene association analysis is exposing the biological relevant association between different genes under different experimental samples. Mining association rules has been applied successfully in various types of data for determining interesting association pattern. Frequent pattern mining is becoming a potential approach in microarray gene expression analysis. In this paper the most relevant mining association rules as well as main issues when discovering efficient and practical method for microarray gene association analysis have been reviewed.