| Инд. авторы: | Voropaeva O.F., Shokin Yu.I., Nepomnyaschchikh L.M., Senchukova S.R. |
| Заглавие: | Mathematical modeling of the tumor markers network |
| Библ. ссылка: | Voropaeva O.F., Shokin Yu.I., Nepomnyaschchikh L.M., Senchukova S.R. Mathematical modeling of the tumor markers network // Proceedings - 2015 International Conference on Biomedical Engineering and Computational Technologies, SIBIRCON 2015. - 2015: Institute of Electrical and Electronics Engineers Inc. - P.225-229. - ISBN: 978-1-4673-9109-2. |
| Внешние системы: | DOI: 10.1109/SIBIRCON.2015.7361888; РИНЦ: 27153593; SCOPUS: 2-s2.0-84969232905; WoS: 000380436300050; |
| Реферат: | eng: A novel method of ensemble clustering for
hyperspectral image segmentation is proposed. The basic idea of
the method is to use a series of k-means algorithms as a
preliminary step to reduce the amount of data under analysis.
Clustering results on real hyperspectral image demonstrate the
efficiency of the proposed algorithms. |
| Ключевые слова: | centroids; clustering ensemble; k-means; hyperspectral image analysis; |
| Издано: | 2015 |
| Физ. характеристика: | с.225-229 |
| Конференция: | Название: 2015 International Conference on Biomedical Engineering and Computational Technologies Аббревиатура: SIBIRCON / SibMedInfo Город: Новосибирск Страна: Россия Даты проведения: 2015-10-28 - 2015-10-30 Ссылка: http://soramn.org/sibmedinfo/ |
| Цитирование: | 1. A.K. Jain, “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, Vol. 31(8), 2010. pp. 651-666. 2. J. Ghosh, A. Acharya, “Cluster ensembles”, Wiley Interdisciplinary Reviews, Data Mining and Knowledge Discovery, Vol. 1, 2011, pp. 305-315. 3. V. Berikov, “Weighted ensemble of algorithms for complex data clustering”, Pattern Recognition Letters, Vol. 38, 2014, pp. 99-106. 4. A. Plaza, J.A. Benediktsson, J.W., Boardman, J. L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, G. Trianni, “Recent advances in techniques for hyperspectral image processing”, Remote sensing of environment, Vol. 113, 2009, pp. S110-S122. 5. R. Ablin, C.H. Sulochana, “A Survey of Hyperspectral Image Classification in Remote Sensing”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Is. 8, 2013, pp. 2986-3003. 6. Hyperspectral Remote Sensing Scenes . [URL: http://www.ehu.es/ccwintco/index.php/HyperspectralRemote SensingScenes]. 7. J.C. Dunn, “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters”, Journal of Cybernetics, Vol. 3(3), 1973, pp. 32-57. |
