1 research outputs found
Gene Expression Data Knowledge Discovery using Global and Local Clustering
To understand complex biological systems, the research community has produced
huge corpus of gene expression data. A large number of clustering approaches
have been proposed for the analysis of gene expression data. However,
extracting important biological knowledge is still harder. To address this
task, clustering techniques are used. In this paper, hybrid Hierarchical
k-Means algorithm is used for clustering and biclustering gene expression data
is used. To discover both local and global clustering structure biclustering
and clustering algorithms are utilized. A validation technique, Figure of Merit
is used to determine the quality of clustering results. Appropriate knowledge
is mined from the clusters by embedding a BLAST similarity search program into
the clustering and biclustering process. To discover both local and global
clustering structure biclustering and clustering algorithms are utilized. To
determine the quality of clustering results, a validation technique, Figure of
Merit is used. Appropriate knowledge is mined from the clusters by embedding a
BLAST similarity search program into the clustering and biclustering process