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An Improved Semi Supervised Nonnegative Matrix Factorization Based Tumor Clustering with Efficient Infomax ICA Based Gene Selection
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Abstract
In cancer class discovery, tumor clustering becomes an active area of research. An exact identification of the type of tumors is essential for efficient treatment of cancer at the prior stage. Numerous techniques have been proposed and used to examine gene expression data, which clustering algorithms are inappropriate to many real-world problems whereas less amount of domain information only available. Adding the domain knowledge can help a clustering algorithm, to improve the quality of clustering result. In this paper, a semi-supervised non-negative matrix factorization (SS-NMF) structure is proposed for selected gene clustering and the genes are selected using proposed infomax ICA approach. Proposed system gives the tumor clustering result in terms of representing pairwise constraints on a small number of data objects that are to be clustered together while specifying whether “cannot” or “must”. Use of iterative procedure perform symmetric trifactorization data similarity matrix to conclude the cluster result. Finally, the experiments on the gene expression dataset verified that the proposed scheme can attain improved clustering results
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