| Инд. авторы: | Tatarnikov V., Berikov V., Pestunov I. |
| Заглавие: | Cluster ensemble construction with the algorithm of averaged centroids |
| Библ. ссылка: | Tatarnikov V., Berikov V., Pestunov I. Cluster ensemble construction with the algorithm of averaged centroids // Proceedings of 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON): Novosibirsk, 18-22 Sep 2017. - 2017. - P.342-345. |
| Внешние системы: | DOI: 10.1109/SIBIRCON.2017.8109902; РИНЦ: 34872558; SCOPUS: 2-s2.0-85040532588; WoS: 000426816500078; |
| Реферат: | eng: The task of finding consensus solution of cluster analysis problem is considered in the paper. A heuristic algorithm for constructing consensus clustering partition using any centroid-based algorithm is proposed. It is theoretically proved that the algorithm is statistically stable. The novelty of the algorithm is that it is implemented and optimized for running in parallel and distributed environment. The paper includes the results of testing the algorithm on artificial and real data. © 2017 IEEE. |
| Ключевые слова: | Clustering algorithms; Running-in; K-means; Distributed environments; Consensus solutions; Consensus clustering; Cluster ensembles; Centroid; Hyperspectral imaging; Heuristic algorithms; Cluster analysis; K-means; Hyperspectral image analysis; Cluster ensemble; Centroid; Spectroscopy; |
| Издано: | 2017 |
| Физ. характеристика: | с.342-345 |
| Конференция: | Название: 2017 International Multi-Conference on Engineering, Computer and Information Sciences Аббревиатура: SIBIRCON Город: Novosibirsk Страна: Russia Даты проведения: 2017-09-18 - 2017-09-22 |
| Цитирование: | 1. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer; 2009. 2. Rui Xu, Donald C. Wunsch. Clustering. Wiley; 2009. 3. Belim S, Kutlunin P. Boundary extraction in images using a clustering algorithm. Computer Optics. 2015; 39(1): 119-124. 4. Jain A.K. Data clustering: 50 years beyond K-means. Pattern Recognition Letters. 2010; 31(8): 651-666. 5. Ghaemi R, Sulaiman M, Ibrahim H, Mustapha N. A Survey: Clustering Ensembles Techniques. World Academy of Science, Engineering and Technology. 2009; 3(2): 535-544. 6. Hore P, Hall L, Goldgof D. A scalable framework for cluster ensembles. Pattern Recognition. 2009; 42: 676-688. 7. Kashef R, Kamel M. Cooperative clustering. Pattern Recognition. 2010; 43: 2315-2329. 8. Jia J, Liu B, Jiao L. Soft spectral clustering ensemble applied to image segmentation. Frontier of Computer Science in China. 2011; 5(1): 66-78. 9. Franek L, Jiang X. Ensemble clustering by means of clustering embedding in vectorspaces. Pattern Recognition. 2014; 47: 833-842. 10. Berikov V, Pestunov I. Ensemble clustering based on weighted coassociation matrices: Error bound and convergence properties. Pattern Recognition. 2017; 63: 427-436. 11. Ghosh J, Acharya A. Cluster ensembles. WIREs Data Mining Knowledge Discovery. 2011; 1: 305-315. 12. Pestunov I, Kulikova E, Rylov S, Berikov V. Ensemble of lustering algorithms for large datasets. Optoelectronics, Instrumentation and Data Processing. 2011; 47(3): 245-252. 13. Pestunov I.A., Rylov S.A., Berikov V.B. Hierarchical clustering algorithms for segmentation of multispectral images. Optoelectronics, Instrumentation and Data Processing. 2015; 51(4): 329-338. 14. Pestunov I.A., Berikov V.B., Sinyavskiy Y.N. Algorithm for multispectral image segmentation based on ensemble of nonparametric clustering algorithms . Vestnik SibGAU. 2010; 5(31): 56-64. 15. Hyperspectral Remote Sensing Scenes. Source: http://www.ehu.eus/ccwintco/index.php |