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Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach

Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach

Miao Wang, Xuequn Shang, Shaohua Zhang, Zhanhuai Li
Copyright: © 2010 |Volume: 1 |Issue: 4 |Pages: 20
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781613502921|DOI: 10.4018/jkdb.2010100104
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MLA

Wang, Miao, et al. "Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach." IJKDB vol.1, no.4 2010: pp.69-88. http://doi.org/10.4018/jkdb.2010100104

APA

Wang, M., Shang, X., Zhang, S., & Li, Z. (2010). Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 1(4), 69-88. http://doi.org/10.4018/jkdb.2010100104

Chicago

Wang, Miao, et al. "Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 1, no.4: 69-88. http://doi.org/10.4018/jkdb.2010100104

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Abstract

DNA microarray technology has generated a large number of gene expression data. Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. It finds clusters of genes possessing similar characteristics together with biological conditions creating these similarities. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, the authors propose the FDCluster algorithm in order to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses Apriori property and several novel techniques for pruning to mine biclusters efficiently. To increase the space usage, FDCluster also utilizes several techniques to generate frequent closed bicluster without candidate maintenance in memory. The experimental results show that FDCluster is more effective than traditional methods in either single micorarray dataset or multiple microarray datasets. This paper tests the biological significance using GO to show the proposed method is able to produce biologically relevant biclusters.

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