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Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis.

, , and . IEEE/ACM Trans. Comput. Biology Bioinform., 10 (4): 845-857 (2013)

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Impact of missing data imputation methods on gene expression clustering and classification., , and . BMC Bioinformatics, (2015)A Comparative Study on the Use of Correlation Coefficients for Redundant Feature Elimination., , , and . SBRN, page 13-18. IEEE Computer Society, (2010)Evaluating Correlation Coefficients for Clustering Gene Expression Profiles of Cancer., , and . BSB, volume 7409 of Lecture Notes in Computer Science, page 120-131. Springer, (2012)A bottom-up oblique decision tree induction algorithm., , , and . ISDA, page 450-456. IEEE, (2011)Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis., , and . IEEE/ACM Trans. Comput. Biology Bioinform., 10 (4): 845-857 (2013)On the selection of appropriate distances for gene expression data clustering., , and . BMC Bioinformatics, 15 (S-2): S2 (2014)On the combination of relative clustering validity criteria., , and . SSDBM, page 4:1-4:12. ACM, (2013)A Cluster Based Hybrid Feature Selection Approach., and . BRACIS, page 43-48. IEEE, (2015)On strategies for building effective ensembles of relative clustering validity criteria., , , , , and . Knowl. Inf. Syst., 47 (2): 329-354 (2016)A framework for bottom-up induction of oblique decision trees., , , and . Neurocomputing, (2014)