Open AccessASSOCIATION RULE MINING FOR GENE EXPRESSION DATA
Author(s) -
Ompriya Kale,
B. F. Momin
Publication year - 2014
Publication title -
international journal of computer science and informatics
Language(s) - English
Resource type - Journals
ISSN - 2231-5292
DOI - 10.47893/ijcsi.2014.1152
Subject(s) - association rule learning , data mining , computer science , scalability , microarray analysis techniques , set (abstract data type) , gene , biology , database , gene expression , genetics , programming language
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.