Moskva, Russian Federation
GRNTI 50.07 Теоретические основы вычислительной техники
BBK 3297 Вычислительная техника
The paper considers the tasks of visual analysis of multidimensional data sets of medical origin. For visual analysis, the approach of building elastic maps is used. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. To improve the results, a number of previously developed procedures are used - preliminary data filtering, removal of separated clusters (flotation). To solve the scalability problem, when the elastic map is adjusted both to the region of condensation of data points and to separately located points of the data cloud, the quasi-Zoom approach is applied. The illustrations of applying elastic maps to various sets of medical data are presented.
multidimensional data, visual analysis, elastic maps, quasi-Zoom
1. Bondarev, A.E. et al, 2016. Visual analysis of clusters for amultidimensional textual dataset. Scientific Visualization.8(3), 1-24.
2. Bondarev, A.E., 2017. Visual analysis and processing ofclusters structures in multidimensional datasets. ISPRSArchives, XLII-2/W4, 151-154.
3. Bondarev, A. E.: The procedures of visual analysis formultidimensional data volumes, Int. Arch. Photogramm.Remote Sens. Spatial Inf. Sci., XLII-2/W12, 17-21,doi.org/10.5194/isprs-archives-XLII-2-W12-17-2019
4. Bondarev, A.E., Bondarenko, A.V., Galaktionov, V.A.,2018. Visual analysis procedures for multidimensional data.Scientific Visualization 10(4), 109 - 122,doi.org/10.26583/sv.10.4.09.
5. Bondarev, A.E., Galaktionov, V.A., 2015a. Analysis ofSpace-Time Structures Appearance for Non-StationaryCFD Problems. Procedia Computer Science, 51, 1801–1810.
6. Bondarev, A.E., Galaktionov, V.A., 2015b.Multidimensional data analysis and visualization for timedependent CFD problems. Programming and ComputerSoftware, 41(5), 247–252,doi.org/10.1134/S0361768815050023.
7. Crisóstomo, J. et al., 2016. Hyperresistinemia and metabolicdysregulation: a risky crosstalk in obese breast cancer.Endocrine, 53(2), 433-442, doi.org/10.1007/s12020-016-0893-x
8. Gorban, A. et al, 2007. Principal Manifolds for DataVisualisation and Dimension Reduction, Springer, Berlin –Heidelberg – New York, 2007.
9. Gorban A., Zinovyev A., 2010. Principal manifolds andgraphs in practice: from molecular biology to dynamicalsystems. International Journal of Neural Systems, 20(3),219–232.
10. Jossinet, J., 1996. Variability of impedivity in normal andpathological breast tissue. Med. & Biol. Eng. & Comput,34, 346-350.
11. Niedoba, T., 2014. Multi-parameter data visualization bymeans of principal component analysis (PCA) in qualitativeevaluation of various coal types / PhysicochemicalProblems of Mineral Processing, 50(2), 575-589.
12. Patrício, M., et al 2018. Using Resistin, glucose, age andBMI to predict the presence of breast cancer. BMC Cancer,18(1), doi.org/10.1186/s12885-017-3877-1.
13. Rocha Neto, A., Barreto, G., 2009. On the Application ofEnsembles of Classifiers to the Diagnosis of Pathologies ofthe Vertebral Column: A Comparative Analysis, IEEE LatinAmerica Transactions, 7(4), 487-496.
14. Silva, J.E., Marques de Sá, J.P., Jossinet, J., 2000.Classification of Breast Tissue by Electrical ImpedanceSpectroscopy. Med & Bio Eng & Computing, 38, 26-30.
15. Thabtah, F., 2017. Machine learning in autistic spectrumdisorder behavioral research: A review and ways forward.Informatics for Health and Social Care, doi.org/10.1080/17538157.2017.1399132
16. UCI Machine Learning Repository, 2019.archive.ics.uci.edu/ml/ (01 March 2019).
17. ViDaExpert, 2019. bioinfo.curie.fr/projects/vidaexpert (01March 2019).
18. Zinovyev, A., 2000. Vizualizacija mnogomernyh dannyh [Visualization of multidimensional data]. Krasnoyarsk, publ. NGTU. 2000. 180 p. [In Russian].
