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05367305 Kong, Xiangzhen Zheng, Chunhou Wu, Yuqiang Wang, Yutian Improving tumor clustering based on gene selection. Huang, De-Shuang (ed.) et al., Advanced intelligent computing theories and applications. With aspects of theoretical and methodological issues. 4th international conference on intelligent computing, ICIC 2008, Shanghai, China, September 15--18, 2008. Proceedings. Berlin: Springer (ISBN 978-3-540-87440-9/pbk). Lecture Notes in Computer Science 5226, 39-46 (2008). 2008 Berlin: Springer EN gene expression data clustering independent component analysis non-negative matrix factorization

doi:10.1007/978-3-540-87442-3_6

Summary: Tumor clustering is becoming a powerful method in cancer class discovery. In this community, non-negative matrix factorization (NMF) has shown its advantages, such as the accuracy and robustness of the representation, over other conventional clustering techniques. Though NMF has shown its efficiency in tumor clustering, there is a considerable room for improvement in clustering accuracy and robustness. In this paper, gene selection and explicitly enforcing sparseness are introduced into clustering process. The independent component analysis (ICA) is employed to select a subset of genes. The unsupervised methods NMF and its extensions, sparse NMF (SNMF) and NMF with sparseness constraint (NMFSC), are then used for tumor clustering on the subset of genes selected by ICA. The experimental results demonstrate the efficiency of the proposed scheme.