A Parallel Consensus Clustering Algorithm

1. Alizadeh, A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)

   Article  Google Scholar 

2. Allison, D.B., et al.: Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet. 7(1), 55–65 (2006)

   Article  MathSciNet  Google Scholar 

3. Ben-Hur, A., Elisseeff, A., Guyon, I.: A stability based method for discovering structure in clustered data. In: Pacific Symposium on Biocomputing, vol. 7 (2001)

   Google Scholar 

4. Bertrand, P., Bel Mufti, G.: Loevinger’s measures of rule quality for assessing cluster stability. Comput. Stat. Data Anal. 50(4), 992–1015 (2006)

   Article  MATH  Google Scholar 

5. Dudoit, S., Fridlyand, J.: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol. 3(7), research0036 (2002)

   Article  Google Scholar 

6. Garge, N., et al.: Reproducible clusters from microarray research: whither? BMC Bioinform. 6(Suppl 2), S10 (2005)

   Article  Google Scholar 

7. Giancarlo, R., Utro, F.: Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis. Theoret. Comput. Sci. 428, 58–79 (2012)

   Article  MATH  MathSciNet  Google Scholar 

8. Giancarlo, R., Scaturro, D., Utro, F.: Computational cluster validation for microarray data analysis: experimental assessment of clest, consensus clustering, figure of merit, gap statistics and model explorer. BMC Bioinform. 9(1), 462 (2008)

   Article  Google Scholar 

9. Giancarlo, R., Utro, F.: Speeding up the Consensus Clustering methodology for microarray data analysis. Algorithms Mol. Biol. 6(1), 1–13 (2011)

   Article  Google Scholar 

10. Giurcaneanu, C.D., Tabus, I.: Cluster structure inference based on clustering stability with applications to microarray data analysis. EURASIP J. Appl. Sig. Process. 2004, 64–80 (2004)

    Article  MATH  Google Scholar 

11. Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15), 3201–3212 (2005)

    Article  Google Scholar 

12. Kustra, R., Zagdanski, A.: Data-fusion in clustering microarray data: balancing discovery and interpretability. IEEE/ACM Trans. Comput. Biol. Bioinf. 7(1), 50–63 (2010)

    Article  Google Scholar 

13. Lange, T., et al.: Stability-based validation of clustering solutions. Neural Comput. 16(6), 1299–1323 (2004)

    Article  MATH  Google Scholar 

14. Levine, E., Domany, E.: Resampling method for unsupervised estimation of cluster validity. Neural Comput. 13(11), 2573–2593 (2001)

    Article  MATH  Google Scholar 

15. Liu, Y., et al.: Understanding of internal clustering validation measures. In: 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE (2010)

    Google Scholar 

16. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)

    Google Scholar 

17. Monti, S., et al.: Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52(1–2), 91–118 (2003)

    Article  MATH  Google Scholar 

18. NCI 60 Cancer Microarray Project. http://genome-www.stanford.edu/NCI60

19. Pirim, H., et al.: Clustering of high throughput gene expression data. Comput. Oper. Res. 39(12), 3046–3061 (2012)

    Article  MathSciNet  Google Scholar 

20. Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98, 15149–15154 (2001)

    Article  Google Scholar 

21. RDevelopment Core Team: R: A language and environment for statistical computing, pp. 1–1731. R Foundation for Statistical Computing, Vienna, Austria (2008)

    Google Scholar 

22. Simpson, T., et al.: Merged consensus clustering to assess and improve class discovery with microarray data. BMC Bioinform. 11(1), 590 (2010)

    Article  Google Scholar 

23. Stevans, W.R.: Advanced Programming in the UNIX Environment. Pearson Education, India (2011)

    Google Scholar 

24. Unold, O., Tagowski, T.: A GPU-based consensus clustering. Glob. J. Comput. Sci. 4(2), 65–69 (2014)

    Google Scholar 

25. Yeoh, E.J., et al.: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1, 133–143 (2002)

    Article  Google Scholar 

Download references