Projected Clustering Algorithm with the Consideration of the Correlation Among Genes
Most of the existing methods perform the projected clustering for gene expression data under the assumption of independence among genes and choose relevant projected space according to individual discriminative score of single gene.In this paper,a novel projected clustering algorithm,namely MOLION,with the consideration of the correlation among genes is proposed to address this issue.Based on the sequential data transformed and a given user-specific favourite function,MOLION employs the branch and bound paradigm to conduct a quick depth-first traverse on the sample enumeration tree while adopting efficient pruning rules and optimization strategy.The experiments conducted on several real Microarray datasets prove the proposed MOLION is of better efficiency and higher prediction accuracy,which provides a fire-new insight into the pathogenesis problem.