Large-scale data analysis using heuristic methods

Informatica

Volumn 22, Issue 1, 2011, Pages 1-10

  Dzemyda, Gintautas ^a   Sakalauskas, Leonidas ^a  

^a VILNIUS UNIVERSITY   (Lithuania)

Author keywords

clustering; heuristics; Markov model; regression; robust statistics; visualization

Indexed keywords

EID: 79954466189     PISSN: 08684952     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (61)

1. Bagirov, A. (2008). Modified global k-means algorithm for minimum sum-of-squares clustering problems. Pattern Recognition, 41(10), 3192-3199.
2. Balys, V., Rudzkis, R. (2010). Statistical classification of scientific publications. Informatica, 21(4), 471-486.
3. Bartkute-Norkuniene, V. (2009). Stochastic optimization algorithms for support vector machines classification. Informatica, 20(2), 173-186.
4. Belkin, M., Niyogi, P. (2003). Laplacian eigenmaps for dimensionality. Speech Communication, 1(2-3), 349-367.
5. Bernatavičiene, J., Dzemyda, G., Marcinkevičius, V. (2007a). Conditions for optimal efficiency of relative MDS. Informatica, 18(2), 187-202.
6. Bernatavičienė, J., Dzemyda, G., Marcinkevičius, V. (2007b). Diagonal majorization algorithm: properties and efficiency. Information Technology and Control, 36(4), 353-358.
7. Benati, S. (2011). Heuristic methods for the optimal statistic median problem. Computers and Operations Research, 38(1), 379-386.
8. Borg, I., Groenen, P.J.F. (2005). Modern Multidimensional Scaling, 2nd edn. Springer Series in Statistics, Springer, Berlin.
9. Box, G.E.P. (1979). Robustness in the strategy of scientific model building. In: Launer, R.,Wilkinson, G. (Eds.), Robustness in Statistics. Academic Press, San Diego. pp. 201-235.
10. Brusco, M.J., Singh, R., Steinley, D. (2009). Variable neighborhood search heuristics for selecting a subset of variables in principal component analysis. Psychometrika, 74(4), 705-726.
11. Chen, S.S., Donoho, D.L., Saunders, M.A. (1998). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33-61. (Pubitemid 128689501)
12. Chiyoshi, F., Roberto, D,. Galva, R.D. (2000). A statistical analysis of simulated annealing applied to the p-median problem. Annals of Operations Research, 96, 61-74.
13. Corradi, G., Janssen, J., Manca, R. (2004). Numerical treatment of homogeneous semi-Markov processes in transient case - a straightforward approach. Methodology and Computing in Applied Probability, 6, 233-246.
14. Cox, T.F., Cox, M.A.A. (2001). Multidimensional Scaling, 2nd edn., Chapman Hall/CRC, London/Boca Raton.
15. D'Amico, G., Di Biase, G., Janssen, J, Manca, R. (2011). HIV evolution: a quantification of the effects due to age and to medical progress. Informatica, 22(1), 27-42.
16. De Silva, V., Tenenbaum, J.B. (2003). Global versus local methods for nonlinear dimensionality reduction. In: Becker, S., Thrun, S., Obermayer, K. (Eds.), Advances in Neural Information Processing Systems, Vol. 15. MIT Press, Cambridge. pp. 721-728.
17. Durio, A., Isaia, E.D. (2011). The minimum density power divergence approach in building robust regressionmodels. Informatica, 22(1), 43-56.
18. Dzemyda, G. (2001). Visualization of a set of parameters characterized by their correlation matrix. Computational Statistics and Data Analysis, 36(10), 15-30. (Pubitemid 32296184)
19. Dzemyda, G., Kurasova, O. (2006). Heuristic approach for minimizing the projection error in the integrated mapping. European Journal of Operational Research, 171(3), 859-878. (Pubitemid 43136697)
20. Dzemyda, G., Marcinkevičius, V., Medvedev, V. (2011). Web application for large-scale multidimensional data visualization. Mathematical Modelling and Analysis (accepted).
21. Han, D., Choi, H., Park, C., Choe, Y. (2009). Fast and accurate retinal vasculature tracing and kernel-Isomapbased feature selection. In: Proceedings of the 2009 International Joint Conference on Neural Networks, IJCNN'09. IEEE, New York. pp. 1075-1082.
22. Garg, L., McClean, S., Meenan, B., Millard, P. (2011). Phase-type survival trees and mixed distribution survival trees for clustering patients' hospital length of stay. Informatica, 22(1), 57-72.
23. Guermeur, Y. (2010). Sample complexity of classifiers taking values in RQ, application to multi-class SVMs. Communications in Statistics - Theory and Methods, 39(3), 543-557.
24. Guermeur, Y., Monfrini, E. (2011). A quadratic loss multi-class SVM for which a radius-margin bound applies. Informatica, 22(1), 73-96.
25. Ivanikovas, S., Dzemyda, G., Medvedev, V. (2008). Large datasets visualization with neural network using clustered training data. In: Advances in Databases and Information Systems. Lecture Notes in Computer Science, Vol. 5207. Springer, Berlin. pp. 143-152.
26. Jessop, A (2010). An optimising approach to alternative clustering schemes. Central European Journal of Operations Research, 18(3), 293-309.
27. Jolliffe, I.T. (2002). Principal Component Analysis. Springer, Berlin.
28. Kaklauskas, A., Zavadskas, E.K., Naimavičiene, J., Krutinis, M., Plakys, V., Venskus, D. (2010). Model for a complex analysis of intelligent built environment. Automation in Construction, 19(3), 326-340.
29. Kanapeckienė, L., Kaklauskas, A., Zavadskas, E.K., Seniut, M. (2010). Integrated knowledge management model and system for construction projects. Engineering Applications of Artificial Intelligence, 23(7), 1200-1215.
30. Karbauskaitė, R., Dzemyda, G. (2009a). Topology preservation measures in the visualization of manifoldtype multidimensional data. Informatica, 20(2), 235-254.
31. Karbauskaitė, R., Dzemyda, G. (2009b). Dependence of the Laplacian eigenmaps method and its modification on the parameters. In: Proc. of the XIIIth International Conference "Applied Stochastic Models and Data Analysis", ASMDA-2009. Technika, Vilnius. pp. 263-268.
32. Karbauskaitė, R., Dzemyda, G., Marcinkevičius, V. (2010). Dependence of locally linear embedding on the regularization parameter. TOP, An Official Journal of the Spanish Society of Statistics and Operations Research, 18(2), 354-376.
33. Kleijnen, J.P.C., den Hertog, D., Angun, E. (2004). Response surface methodology's steepest ascent and step size revisited. European Journal of Operational Research, 159(1), 121-131.
34. Kohonen, T. (2001). Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, Vol. 30. Springer, Berlin.
35. Kubilius, K., Melichov, D. (2011). On comparison of the estimators of the Hurst index of the solutions of stochastic differential equations driven by the fractional Brownian motion, Informatica, 22(1), 97-114.
36. Kurasova, O., Molytė, A. (2011). Quality of quantization and visualization of vectors obtained by neural gas and self-organizing map. Informatica, 22(1), 115-134.
37. Liang, T., Szeto, K.Y. (2011). Community detection through optimal density contrast of adjacency matrix. Informatica, 22(1), 135-148.
38. Mao, J., Jain, A.K. (1995). Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. Neural Networks, 6, 296-317.
39. Mathieu, E., Foucher, Y., Dellamonica, P., Daures, J.P. (2007). Parametric and nonhomogeneous semi-Markov process for HIV control. Methodology and Computing Applied Probability, 9(3), 389-397.
40. Medvedev, V., Dzemyda, G. (2006). Optimization of the local search in the training for SAMANN neural network. Journal of Global Optimization, 35, 607-623. (Pubitemid 44078248)
41. Meiri, R., Zahavi, J. (2006). Using simulated annealing to optimize the feature selection problem in marketing applications. European Journal of Operational Research, 171(3), 842-858. (Pubitemid 43132950)
42. Michalewicz, Z., Fogel, D.B. (2004). How to Solve It: Modern Heuristics. Springer, Berlin.
43. Naud, A. (2004). Visualization of high-dimensional data using a association of multidimensional scaling to clustering. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Vol. 1. pp. 252-255. (Pubitemid 40068618)
44. Neubauer, J., Vesely, V. (2011). Change point detection by sparse parameter estimation. Informatica, 22(1), 149-164.
45. Nunkesser, R, Morell, O. (2010). An evolutionary algorithms for robust regression. Computational Statistics and Data Analysis, 54(12), 3242-3248.
46. Pearl, J. (1983). Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading.
47. Sakalauskas, L., Krarup, J. (2006). Heuristic and stochastic methods in optimization. European Journal of Operational Research, 171(3), 723-890.
48. Sakalauskas, L., Zavadskas, E. (2009). Optimization and intelligent decisions. Technological and Economic Development of Economy, 15(2), 189-196.
49. Saul, L.K, Roweis, S.T. (2003). Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research, 4, 119-155.
50. Stephanie, B., El Mostafa, Q., Lupo Coralie, L., Mohamed, H. (2011). From multiblock partial least squares to multiblock redundancy analysis: a continuum approach. Informatica, 22(2) (accepted).
51. Tanahashi, Y., Chen, C.-K., Marchesin, S., Ma, K.-L. (2010). An interface design for future cloud-based visualization services. In: Proceedings of 2010 IEEE Second International Conference on Cloud Computing Technology and Science. IEEE, New York. pp. 609-613.
52. Tenenbaum, J.B., de Silva, V., Langford, J.C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319-2323. (Pubitemid 32041577)
53. Trosset, M.W., Groenen, P.J.F. (2005). Multidimensional scaling algorithms for large data sets. Computing Science and Statistics, 37.
54. Tversky, A.; Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, New Series, 185(4157), 1124-1131.
55. Vaidogas, E.R., Skėnaite, J. (2010). Protecting built property against fire disasters: multi-attribute decision making with respect to fire risk. International Journal of Strategic Property Management, 14(4), 391-407.
56. Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, Berlin.
57. Williams,M., Munzner, T. (2004). Steerable, progressive multidimensional scaling. In: Proceedings of the IEEE Symposium on Information Visualization, INFOVIS'04. IEEE, New York. pp. 57-64. (Pubitemid 40449128)
58. Winker, P., Gilli, M. (2004). Application of optimization heuristics to estimation and modeling problems. Computational Statistics and Data Analysis, 47(2), 211-223.
59. Xavier, A.E. (2010). The hyperbolic smoothing clustering method. Pattern Recognition, 43(3), 731-737.
60. Zavadskas, E.K., Kaklauskas, A., Vilutienė, T. (2009).Multicriteria evaluation of apartment blocks maintenance contractors: Lithuanian case study. International Journal of Strategic Property Management, 13(4), 319-338.
61. Zavadskas, E.K., Vaidogas, E.R. (2009). Multiattribute selection from alternative designs of infrastructure components for accidental situations. Computer-Aided Civil and Infrastructure Engineering, 24(5), 346-358.