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A deep learning object detection method to improve cluster analysis of two-dimensional data
dc.contributor.author | Couturier, Raphaël | |
dc.contributor.author | Gregori, Pablo | |
dc.contributor.author | Noura, Hassan | |
dc.contributor.author | Salman, Ola | |
dc.contributor.author | Sider, Abderrahmane | |
dc.date.accessioned | 2024-06-03T07:34:55Z | |
dc.date.available | 2024-06-03T07:34:55Z | |
dc.date.issued | 2024-02-07 | |
dc.identifier.citation | 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/s11042-024-18148-5 | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/207572 | |
dc.description.abstract | 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. | ca_CA |
dc.format.extent | 20 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer | ca_CA |
dc.relation.isPartOf | Multimed Tools Appl (2024). | ca_CA |
dc.rights | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | Clustering algorithms | ca_CA |
dc.subject | Clustering initialization methods | ca_CA |
dc.subject | Clustering initialization metrics | ca_CA |
dc.subject | Deep learning object detection model | ca_CA |
dc.title | A deep learning object detection method to improve cluster analysis of two-dimensional data | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s11042-024-18148-5 | |
dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | ca_CA |
dc.relation.publisherVersion | https://link.springer.com/article/10.1007/s11042-024-18148-5 | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
project.funder.name | EIPHI Graduate School | ca_CA |
project.funder.name | General Directorate for Scientific Research and Technological Development, Ministry of Higher Education and Scientific Research (DGRSDT), Algeria | ca_CA |
oaire.awardNumber | ANER 2022 AGRO-IA-LIMENTAIRE | ca_CA |
oaire.awardNumber | ANR-17-EURE-0002 | ca_CA |
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