FARMER: Finding Interesting Rule Groups in Microarray Datasets

Microarray datasets typically contain large number of columns but small number of rows. Association rules have been proved to be useful in analyzing such datasets. However, most existing association rule mining algorithms are unable to efficiently handle datasets with large number of columns. Moreover, the number of association rules generated from such datasets is enormous due to the large number of possible column combinations. In this paper, we describe a new algorithm called FARMER that is specially designed to discover association rules from microarray datasets. Instead of finding individual association rules, FARMER finds interesting rule groups which are essentially a set of rules that are generated from the same set of rows. Unlike conventional rule mining algorithms, FARMER searches for interesting rules in the row enumeration space and exploits all user-specified constraints including minimum support, confidence and chi-square to support efficient pruning. Several experiment...

• Type: Conference
• Year: 2004
• Where: SIGMOD
• Authors: Gao Cong, Anthony K. H. Tung, Xin Xu, Feng Pan, Jiong Yang
• Keywords: Association Rule Mining, Database, Microarray Datasets, Rule Mining Algorithms, SIGMOD 2004