| Инд. авторы: | Pestunov I.A., Rylov S.A., Sinyavskiy Yu.N., Berikov V.B. |
| Заглавие: | Computationally efficient methods of clustering ensemble construction for satellite image segmentation |
| Библ. ссылка: | Pestunov I.A., Rylov S.A., Sinyavskiy Yu.N., Berikov V.B. Computationally efficient methods of clustering ensemble construction for satellite image segmentation // CEUR Workshop Proceedings. - 2017. - Vol.1901. - P.194-200. - ISSN 1613-0073. |
| Внешние системы: | РИНЦ: 31047679; SCOPUS: 2-s2.0-85029327861; |
| Реферат: | eng: Combining multiple partitions into single ensemble clustering solution is a prominent way to improve accuracy and stability of clustering solutions. One of the major problems in constructing clustering ensembles is high computational complexity of the common methods. In this paper two computationally efficient methods of constructing ensembles of nonparametric clustering algorithms are introduced. They are based on the use of co-association matrix and subclusters. The results of experiments on synthetic and real datasets confirm their effectiveness and show the stability of the obtained solutions. The performance of the proposed methods allows to process large images including multispectral satellite data. |
| Ключевые слова: | Image processing; Computational efficiency; Cluster analysis; Cobalt compounds; Image segmentation; Nanotechnology; Clustering algorithms; Non-parametric; Multispectral satellite data; Multispectral images; Ensemble clustering; Ensemble; Computationally efficient; Co-association matrix; Clustering solutions; Security of data; Nonparametric clustering algorithm; Multispectral image segmentation; Ensemble; Co-association matrix; |
| Издано: | 2017 |
| Физ. характеристика: | с.194-200 |
| Конференция: | Название: Информационные технологии и нанотехнологии Аббревиатура: ИТНТ-2017 Город: Самара Даты проведения: 2017-04-25 - 2017-04-27 |
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