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A deep learning object detection method to improve cluster analysis of two-dimensional data.

Authors :
Couturier, Raphaël
Gregori, Pablo
Noura, Hassan
Salman, Ola
Sider, Abderrahmane
Source :
Multimedia Tools & Applications; Aug2024, Vol. 83 Issue 28, p71171-71187, 17p
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13807501
Volume :
83
Issue :
28
Database :
Complementary Index
Journal :
Multimedia Tools & Applications
Publication Type :
Academic Journal
Accession number :
178777886
Full Text :
https://doi.org/10.1007/s11042-024-18148-5