Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/42172
Title: FARMER: Finding interesting rule groups in microarray datasets
Authors: Cong, G. 
Tung, A.K.H. 
Xu, X. 
Pan, F.
Yang, J.
Issue Date: 2004
Source: Cong, G.,Tung, A.K.H.,Xu, X.,Pan, F.,Yang, J. (2004). FARMER: Finding interesting rule groups in microarray datasets. Proceedings of the ACM SIGMOD International Conference on Management of Data : 143-154. ScholarBank@NUS Repository.
Abstract: 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 experiments on real bioinformatics datasets show that FARMER is orders of magnitude faster than previous association rule mining algorithms.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/42172
ISSN: 07308078
Appears in Collections:Staff Publications

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