Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral clustering and its use in bioinformatics
Journal of Computational and Applied Mathematics
Techniques for clustering gene expression data
Computers in Biology and Medicine
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
Artificial Intelligence in Medicine
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
Tumor clustering using nonnegative matrix factorization with gene selection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Exploratory Consensus of Hierarchical Clusterings for Melanoma and Breast Cancer
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bioinformatics
Data Mining on DNA Sequences of Hepatitis B Virus
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Molecular Pattern Discovery Based on Penalized Matrix Decomposition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mutual Information-Based Supervised Attribute Clustering for Microarray Sample Classification
IEEE Transactions on Knowledge and Data Engineering
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
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In order to perform successful diagnosis and treatment of cancer, discovering, and classifying cancer types correctly is essential. One of the challenging properties of class discovery from cancer data sets is that cancer gene expression profiles not only include a large number of genes, but also contains a lot of noisy genes. In order to reduce the effect of noisy genes in cancer gene expression profiles, we propose two new consensus clustering frameworks, named as triple spectral clustering-based consensus clustering (SC^{3}) and double spectral clustering-based consensus clustering (SC^{2}Ncut) in this paper, for cancer discovery from gene expression profiles. SC^{3} integrates the spectral clustering (SC) algorithm multiple times into the ensemble framework to process gene expression profiles. Specifically, spectral clustering is applied to perform clustering on the gene dimension and the cancer sample dimension, and also used as the consensus function to partition the consensus matrix constructed from multiple clustering solutions. Compared with SC^{3}, SC^{2}Ncut adopts the normalized cut algorithm, instead of spectral clustering, as the consensus function. Experiments on both synthetic data sets and real cancer gene expression profiles illustrate that the proposed approaches not only achieve good performance on gene expression profiles, but also outperforms most of the existing approaches in the process of class discovery from these profiles.