Microarray technology has created a revolution in the field of biological research. Association rules can not only group the similarly expressed genes but also discern relationships among genes. We propose a new row-enumeration rule mining method to mine high confidence rules from microarray data. It is a support-free algorithm that directly uses the confidence measure to effectively prune the search space. Experiments on Leukemia microarray data set show that proposed algorithm outperforms support-based rule mining with respect to scalability and rule extraction.
KALE, O. V. and MOMIN, B. F.
"ASSOCIATION RULE MINING FOR GENE EXPRESSION DATA,"
International Journal of Computer Science and Informatics: Vol. 3
, Article 4.
Available at: https://www.interscience.in/ijcsi/vol3/iss4/4