Paper Info
Title
FDCluster: Mining Frequent Closed Discriminative Bicluster without Candidate Maintenance in Multiple Microarray Datasets
Abstract
Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, we propose an algorithm, FDCluster, to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses Apriori property and several novel techniques for pruning to mine frequent closed bicluster without candidate maintenance. The experimental results show that FDCluster is more effectiveness than traditional method in either single micorarray dataset or multiple microarray datasets. We also test the biological significance using GO to show our proposed method is able to produce biologically relevant biclusters.
Year
DOI
Venue
2010
10.1109/ICDMW.2010.10
ICDM Workshops
Keywords
Field
DocType
biologically relevant biclusters,biological significance,condition set,pattern clustering,biological biclusters,frequent closed bicluster,frequent closed discriminative bicluster mining,candidate maintenance,apriori property,microarray dataset,fdcluster,biclustering,weighted undirected sample relational graph,frequent closed discriminative bicluster,multiple microarray datasets,microarray,data mining,lab-on-a-chip,gene set point,bioinformatics,pruning,lab on a chip,maintenance engineering,algorithm design and analysis,scalability,gene expression
Data mining,Algorithm design,Microarray,Pattern recognition,Computer science,Pattern clustering,A priori and a posteriori,Artificial intelligence,Biclustering,Cluster analysis,Discriminative model,Scalability
Conference
ISBN
Citations 
PageRank 
978-0-7695-4257-7
5
0.47
References 
Authors
14
4
Name
Order
Citations
PageRank
Miao Wang150.47
Xuequn Shang29929.07
Shaohua Zhang383.61
Zhanhuai Li427051.04