TY - JFULL AU - M. Anandhavalli Gauthaman PY - 2008/7/ TI - Analysis of DNA Microarray Data using Association Rules: A Selective Study T2 - International Journal of Computer and Information Engineering SP - 1779 EP - 1784 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/5818 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 18, 2008 N2 - DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data. ER -