A deep learning object detection method to improve cluster analysis of two-dimensional data
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
A deep learning object detection method to improve cluster analysis of two-dimensional dataDate
2024-02-07Publisher
SpringerBibliographic 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/s11042-024-18148-5Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s11042-024-18148-5Version
info:eu-repo/semantics/acceptedVersionSubject
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 ... [+]
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. [-]
Is part of
Multimed Tools Appl (2024).Funder Name
EIPHI Graduate School | General Directorate for Scientific Research and Technological Development, Ministry of Higher Education and Scientific Research (DGRSDT), Algeria
Project code
ANER 2022 AGRO-IA-LIMENTAIRE | ANR-17-EURE-0002
Rights
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
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