Coclustering-a useful tool for chemometrics

Journal of Chemometrics

Volumn 26, Issue 6, 2012, Pages 256-263

  Bro, Rasmus ^a   Papalexakis, Evangelos E ^b   Acar, Evrim ^a   Sidiropoulos, Nicholas D ^c  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b Carnegie Mellon University ^*  (United States)

^c University of Minnesota   (United States)

Author keywords

Clustering; Coclustering; L1 norm; Sparsity

Indexed keywords

'OMICS'; CHEMOMETRICES; CLUSTERINGS; CO-CLUSTERING; DATA SET; ENVIRONMENTAL ANALYSIS; HIER-ARCHICAL CLUSTERING; L1 NORM; PRINCIPAL-COMPONENT ANALYSIS; SPARSITY;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84862756829     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1424     Document Type: Article

References (21)

1. Banerjee A, Merugu S, Dhillon IS, Ghosh J. Clustering with Bregman divergences. J. Mach. Learn Res. 2005; 6: 1705-1749.
2. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, Loda M, Weber G, Mark EJ, Lander ER, Wong W, Johnson BE, Golub TR, Sugarbaker DJ, Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. U.S.A. 2001; 98: 13790-13795.
3. Cho H, Dhillon IS, Guan Y, Sra S. Minimum sum-squared residue co-clustering of gene expression data. Proceedings of the Fourth SIAM International Conference on Data Mining 2004; 114-125.
4. Damian D, Oresic M, Verheij E, Meulman J, Friedman J, Adourian A, Morel N, Smilde A, van der Greef J. Applications of a new subspace clustering algorithm (COSA) in medical systems biology. Metabolomics 2007; 3: 69-77.
5. de la Mata-Espinosa P, Bosque-Sendra JM, Bro R, Cuadros-Rodriguez L. Discriminating olive and non-olive oils using HPLC-CAD and chemometrics. Anal. Bioanal. Chem. 2011a\; 399: 2083-2092.
6. de la Mata-Espinosa P, Bosque-Sendra JM, Bro R, Cuadros-Rodriguez L. Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools. Talanta 2011b; 85: 177-182.
7. Dhillon IS. Co-clustering documents and words using bipartite spectral graph partitioning. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining 2001; 269-274.
8. Dhillon IS, Mallela S, Modha DS. Information-theoretic co-clustering. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2003; 89-98.
9. Friedman JH, Meulman JJ. Clustering objects on subsets of attributes. J. Roy. Stat. Soc. B Stat. Meth. 2004; 66: 815-849.
10. Hageman JA, van den Berg RA, Westerhuis JA, van der Werf MJ, Smilde AK. Genetic algorithm based two-mode clustering of metabolomics data. Metabolomics 2008; 4: 141-149.
11. Hartigan JA. Direct clustering of a data matrix. J. Am. Stat. Assoc. 1972; 67: 123-129.
12. Kaiser HF. The varimax criterion for analytic rotation in factor analysis. Psychometrika 1958; 23: 187-200.
13. Lee M, Shen H, Huang JZ, Marron JS. Biclustering via sparse singular value decomposition. Biometrics 2010; 66: 1087-1095.
14. Liu Y, Hayes DN, Nobel A, Marron JS. Statistical significance of clustering for high-dimension, low-sample size data. J. Am. Stat. Assoc. 2008; 103: 1281-1293.
15. Madeira SC, Oliveira AL. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. 2004; 1: 24-45.
16. Osborne MR, Presnell B, Turlach BA. On the LASSO and its dual. J. Comput. Graph. Stat. 2000; 9: 319-337.
17. Papalexakis EE, Sidiropoulos ND. Co-clustering as multilinear decomposition with sparse latent factors. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 2011.
18. Papalexakis EE, Sidiropoulos ND, Garofalakis MN. Reviewer profiling using sparse matrix regression. 2010 IEEE International Conference on Data Mining Workshops, 2010; 1214-1219.
19. Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 1996; 58: 267-288.
20. Witten DM, Tibshirani R, Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 2009; 10: 515-534.
21. Zou H, Hastie T. Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 2005; 67: 301-320.