DATA STREAM UNSUPERVISED PARTITIONING METHOD
| Autor: | Yuding Wang, Kacem Chehdi, Claude Cariou, Benoit Vozel |
|---|---|
| Přispěvatelé: | Institut d'Électronique et des Technologies du numéRique (IETR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE International Geoscience and Remote Sensing Symposium (IGARSS) IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2022, Kuala Lumpur, Malaysia. ⟨10.1109/IGARSS46834.2022.9884448⟩ |
| DOI: | 10.1109/IGARSS46834.2022.9884448⟩ |
| Popis: | International audience; Data stream partitioning has attracted more and more attention in processing large-scale data. The use of parametric methods to perform this task requires for each application an empirical tuning of the different parameters. In practice, this step is difficult to perform and often does not lead to an optimized partitioning result. To avoid this difficulty and provide an objective and optimized partitioning, we propose in this paper an unsupervised and non-parametric algorithm of data stream which employs the Optimized Fuzzy C-Means algorithm. The partitioning is first performed on a series of data chunks and the final partition is obtained by a fusion process of the intermediate classes formed before. The performance of the proposed algorithm is evaluated and compared with a recent state-of-the-art algorithm on hyperspectral image databases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|