On discovery of maximal confident rules without support pruning in microarray data

Authors:
Tara McIntosh;Sanjay Chawla
Affiliations:
The University of Sydney, Sydney, Australia;The University of Sydney, Sydney, Australia
Venue:
Proceedings of the 5th international workshop on Bioinformatics
Year:
2005

Citing 9
Cited 2

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model

Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Using transposition for pattern discovery from microarray data

DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining confident co-location rules without a support threshold

Proceedings of the 2003 ACM symposium on Applied computing
Carpenter: finding closed patterns in long biological datasets

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
FARMER: finding interesting rule groups in microarray datasets

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining Frequent Closed Patterns in Microarray Data

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining top-K covering rule groups for gene expression data

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
GOstat: find statistically overrepresented Gene Ontologies within a group of genes

Bioinformatics

High Confidence Rule Mining for Microarray Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cohesion: A concept and framework for confident association discovery with potential application in microarray mining

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Microarray data provides a perfect riposte to the original assumption underlying association rule mining -- large but sparse transaction sets. In a typical microarray the number of columns (genes) is an order of magnitude larger than the number of rows (experiments). A new family of row enumerated rule mining algorithms have emerged to facilitate mining in dense sets. However, to date, all the algorithms proposed to mine expression relationships alone rely on the support measure to prune the search space. This is a major shortcoming as it results in the pruning of many potentially interesting rules which have low support but high confidence. In this paper we propose the MAXCONF algorithm which exploits the weak downward closure of confidence to directly mine for high confidence rules. We also provide a means to evaluate the biological significance of the gene relationships identified. An evaluation of MAXCONF with RERII on the database BIND shows that their recall is 94% and .15% respectively.