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Application of the Bi-CoPaM Method to Five Escherichia Coli Datasets Generated under Various Biological Conditions., , , and . J. Signal Process. Syst., 79 (2): 159-166 (2015)Exploration of distance metrics in consensus clustering analysis of FMRI data., , , and . DSP, page 1-5. IEEE, (2017)M-N scatter plots technique for evaluating varying-size clusters and setting the parameters of Bi-CoPaM and Uncles methods., , , and . ICASSP, page 6726-6730. IEEE, (2014)Splitting-while-merging framework for clustering high-dimension data with component-wise expectation conditional maximisation., , , and . ICASSP, page 2932-2936. IEEE, (2014)Object based validation algorithm and its application to consensus clustering., , and . EUSIPCO, page 1-5. IEEE, (2013)Clustering consistency in neuroimaging data analysis., , , and . FSKD, page 1118-1122. IEEE, (2015)Comprehensive analysis of multiple microarray datasets by binarization of consensus partition matrix., , , and . MLSP, page 1-6. IEEE, (2012)Scalable clustering based on enhanced-SMART for large-scale FMRI datasets., , , , and . ICASSP, page 962-966. IEEE, (2015)UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets., , , and . BMC Bioinform., (2015)Method for the identification of the subsets of genes specifically consistently co-expressed in a set of datasets., , , and . MLSP, page 1-6. IEEE, (2013)