Investigation of a new GRASP-based clustering algorithm applied to biological data

Computers and Operations Research

Volumn 37, Issue 8, 2010, Pages 1381-1388

  Nascimento, Mariá C V ^a   Toledo, Franklina M B ^a   de Carvalho, André C P L F ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Bioinformatics; Clustering; Gene expression data; GRASP

Indexed keywords

BIOLOGICAL DATA; CLUSTERING; CLUSTERING RESULTS; DATA ANALYSIS; DATA ANALYSIS TECHNIQUES; DATA SETS; GENE EXPRESSION DATA; GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE; MATHEMATICAL FORMULATION; OTHER ALGORITHMS;

BIOACTIVITY; BIOINFORMATICS; CLUSTER ANALYSIS; GENE EXPRESSION;

CLUSTERING ALGORITHMS;

EID: 73449106435     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2009.02.014     Document Type: Article

References (42)

1. Larrañaga P., Borja Calvo R.S., Bielza C., Galdiano J., Inza I., Lozano J.A., et al. Machine learning in bioinformatics. Briefings in Bioinformatics 7 (2006) 86-112
2. Ushioda A., and Kawasaki J. Hierarchical clustering of words and application to NLP tasks. In: Ejerhed E., and Dagan I. (Eds). Fourth workshop on very large corpora (1996), Association for Computational Linguistics, Somerset, New Jersey 28-41
3. White S.D.M., and Frenk C.S. Galaxy formation through hierarchical clustering. Astrophysical Journal 379 Part 1 (1991) 52-79
4. Wu Z., and Leahy R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15 11 (1993) 1101-1113
5. Jain A.K., Murty M.N., and Flynn P.J. Data clustering: a review. ACM Computing Surveys 31 (1999) 264-323
6. Jiang D., Tang C., and Zhang A. Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering 16 11 (2004) 1370-1386
7. Alon U., Barkai N., Notterman D.A., Gish K., Ybarra S., Mack D., et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences USA 96 (1999) 6745-6750
8. Ma P.C.H., Chan K.C.C., Yao X., and Chiu D.K.Y. An evolutionary clustering algorithm for gene expression microarray data analysis. IEEE Transactions on Evolutionary Computation 10 3 (2006) 296-314
9. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Technical Report, Broad Institute, MIT, Kluwer Academic Publishers; 2003.
10. Bandyopadhyay S., and Maulik U. An evolutionary technique based on k-means algorithm for optimal clustering in RN. Information Sciences 146 1 (2002) 221-237
11. Pacheco J. A scatter search approach for the minimum sum-of-squares clustering problem. Computers & Operations Research 32 (2005) 1325-1335
12. Scheuerer S., and Wendolsky R. A scatter search heuristic for the capacitated clustering problem. European Journal of Operational Research 169 2 (2006) 533-547
13. Sultan K.H.A. A tabu search approach to the clustering problem. Pattern Recognition 28 (1995) 1443-1451
14. Feo T., and Resende M. A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8 (1989) 67-71
15. Areibi S., and Vannelli A. A GRASP clustering technique for circuit partitioning. In: Gu J., and Pardalos P. (Eds). Satisfiability problems. DIMACS series on discrete mathematics and theoretical computer science vol. 35 (1997), American Mathematical Society 711-724
16. Laguna M., and Martí R. GRASP and path relinking for 2-layer straight line crossing minimization. Informs Journal on Computing 11 1 (1999) 44-52
17. Cano J., Cordón O., Herrera F., and Sánchez L. A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using k-means as a local search procedure. Journal of Intelligent & Fuzzy Systems 12 (2002) 235-242
18. Li Y., Pardalos P., and Resende M. A greedy randomized adaptive search procedure for the quadratic assignment problem. In: Pardalos P., and Wolkowicz H. (Eds). DIMACS series on discrete mathematics and theoretical computer science vol. 16 (1994), American Mathematical Society 237-261
19. Nascimento MCV, Resende MCG, Toledo FMB. GRASP heuristic with path-relinking for the multi-plant capacitated lot sizing problem, European Journal of Operational Research 2009, to appear, doi:10.1016/j.ejor.2009.01.047.
20. Kaufman L., and Rousseeuw P.J. Finding groups in data: an introduction to cluster analysis (1990), Wiley, New York
21. Brito M., Chfivez E., Quiroz A., and Yukich J. Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection. Statistics & Probability Letters 35 (1997) 33-42
22. Xu Y., Olman V., and Xu D. Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees. Bioinformatics 18 4 (2002) 536-545
23. Ao S.I., Yip K.Y., Ng M.K., Cheung D.W.-L., Fong P.-Y., Melhado I., et al. Clustag: hierarchical clustering and graph methods for selecting tag SNPs. Bioinformatics 21 8 (2005) 1735-1736
24. ILOG. CPLEX 11.0 reference manual. ILOG: France, v. 11.0; 2007.
25. Hubert L., and Arabie P. Comparing partitions. Journal of Classification 2 (1985) 193-218
26. Fisher R.A. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7 (1936) 179-188
27. Vinod H.D. Integer programming and the theory of grouping. Journal of the American Statistical Association 64 326 (1969) 506-519
28. Rao M.R. Cluster analysis and mathematical programming. Journal of the American Statistical Association 66 335 (1971) 622-626
29. Hansen P., and Jaumard B. Cluster analysis and mathematical programming. Mathematical Programming 79 (1997) 191-215
30. Hansen P., and Mladenović N. J-means: a new local search heuristic for minimum sum of squares clustering. Pattern Recognition 34 (2001) 405-413
31. Ding C.H.Q., and Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17 (2001) 349-358
32. Nakai K., and Kanehisa M. Expert system for predicting protein localization sites in gram-negative bacteria. PROTEINS: Structure, Function, and Genetics 11 (1991) 95-110
33. Bennett K.P., and Mangasarian O.L. Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software 1 (1992) 23-34
34. Su A.I., Cooke M.P., Ching K.A., Hakak Y., Walker J.R., Wiltshire T., et al. Large-scale analysis of the human and mouse transcriptomes. Proceedings of the National Academy of Sciences 99 (2002) 4465-4470
35. van 't Veer L.J., Dai H., van de Vijver M.J., He Y.D., Hart A.A., Mao M., et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415 6871 (2002) 530-536
36. West M., Blanchette C., Dressman H., Huang E., Ishida S., Spang R., et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences 98 20 (2001) 11462-11467
37. Monti S., Savage K.J., Kutok J.L., Feuerhake F., Kurtin P., Mihm M., et al. Molecular profiling of diffuse large b-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood 105 (2005) 1851-1861
38. Rosenwald A., Wright G., Chan W.C., Connors J.M., Campo E., Fisher R.I., et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-b-cell lymphoma. The New England Journal of Medicine 346 (2002) 1937-1947
39. Asunción A, Newman D. UCI machine learning repository 〈http://www.ics.uci.edu/mlearn/MLRepository.html〉; 2007.
40. Cancer program data sets. Broad MIT Institute 〈http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi〉.
41. Hoshida Y., Brunet J.-P., Tamayo P., Golub T.R., and Mesiro J.P. Subclass mapping: identifying common subtypes in independent disease data sets. PLOS one 2 11 (2007) e1195
42. Handl J., and Knowles J. An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 11 (2007) 56-76