A deep learning object detection method to improve cluster analysis of two-dimensional data
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Título
A deep learning object detection method to improve cluster analysis of two-dimensional dataFecha de publicación
2024-02-07Editor
SpringerCita bibliográfica
Couturier, R., Gregori, P., Noura, H. et al. A deep learning object detection method to improve cluster analysis of two-dimensional data. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18148-5Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s11042-024-18148-5Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Clustering is an unsupervised machine learning method grouping data samples into clusters
of similar objects, used as a system support tool in numerous applications such as banking
customers profiling, document ... [+]
Clustering is an unsupervised machine learning method grouping data samples into clusters
of similar objects, used as a system support tool in numerous applications such as banking
customers profiling, document retrieval, image segmentation, and e-commerce recommendation engines. The effectiveness of several clustering techniques is sensible to the initialization
parameters, and different solutions have been proposed in the literature to overcome this
limitation. They require high computational memory consumption when dealing with big
data. In this paper, we propose the application of a recent object detection Deep Learning
model (YOLO-v5) for assisting the initialization of classical techniques and improving their
effectiveness on two-variate datasets, leveraging the accuracy and reducing dramatically the
memory and time consumption of classical clustering methods. [-]
Publicado en
Multimed Tools Appl (2024).Entidad financiadora
EIPHI Graduate School | General Directorate for Scientific Research and Technological Development, Ministry of Higher Education and Scientific Research (DGRSDT), Algeria
Código del proyecto o subvención
ANER 2022 AGRO-IA-LIMENTAIRE | ANR-17-EURE-0002
Derechos de acceso
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
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